{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 零基础实战机器学习\n",
    "\n",
    "## 第12讲 RNN预测App的激活数走势\n",
    "\n",
    "作者 黄佳\n",
    "\n",
    "极客时间专栏链接：https://time.geekbang.org/column/intro/438\n",
    "\n",
    "\n",
    "问题：根据App的历史激活（即下载后注册用户并使用App）数字，来预测其未来走势\n",
    "\n",
    "易速鲜花公司拥有过去两年App的日激活数。\n",
    "\n",
    "通过神经网络中的RNN模型，我们可以对这个时序数据集进行预测。\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数据的读入和预处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np #导入NumPy\n",
    "import pandas as pd #导入Pandas "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Activation</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-01-01</th>\n",
       "      <td>419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-02</th>\n",
       "      <td>432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-03</th>\n",
       "      <td>436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-04</th>\n",
       "      <td>439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-05</th>\n",
       "      <td>439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-21</th>\n",
       "      <td>916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-22</th>\n",
       "      <td>925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-23</th>\n",
       "      <td>926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-24</th>\n",
       "      <td>920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-25</th>\n",
       "      <td>932</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>756 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Activation\n",
       "Date                  \n",
       "2019-01-01         419\n",
       "2019-01-02         432\n",
       "2019-01-03         436\n",
       "2019-01-04         439\n",
       "2019-01-05         439\n",
       "...                ...\n",
       "2021-01-21         916\n",
       "2021-01-22         925\n",
       "2021-01-23         926\n",
       "2021-01-24         920\n",
       "2021-01-25         932\n",
       "\n",
       "[756 rows x 1 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_app = pd.read_csv('app.csv', index_col='Date', parse_dates=['Date']) #导入数据\n",
    "df_app #显示数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt #导入matplotlib.pyplot\n",
    "plt.style.use('fivethirtyeight') #设定绘图风格\n",
    "df_app[\"Activation\"].plot(figsize=(12,4),legend=True) #绘制激活数\n",
    "plt.title('App Activation Count') #图题\n",
    "plt.show() #绘图"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数据清洗"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Activation    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_app.isna().sum() #有NaN吗？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(df_app.Activation < 0).values.any() #有负值吗？"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 拆分数据集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 按照2020年10月1日为界拆分数据集\n",
    "Train = df_app[:'2020-09-30'].iloc[:,0:1].values #训练集\n",
    "Test = df_app['2020-10-01':].iloc[:,0:1].values #测试集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[419],\n",
       "       [432],\n",
       "       [436],\n",
       "       [439],\n",
       "       [439],\n",
       "       [435],\n",
       "       [423],\n",
       "       [428],\n",
       "       [421],\n",
       "       [418],\n",
       "       [423],\n",
       "       [424],\n",
       "       [431],\n",
       "       [449],\n",
       "       [442],\n",
       "       [449],\n",
       "       [459],\n",
       "       [464],\n",
       "       [468],\n",
       "       [461],\n",
       "       [453],\n",
       "       [455],\n",
       "       [469],\n",
       "       [461],\n",
       "       [463],\n",
       "       [480],\n",
       "       [478],\n",
       "       [467],\n",
       "       [472],\n",
       "       [474],\n",
       "       [459],\n",
       "       [456],\n",
       "       [455],\n",
       "       [440],\n",
       "       [447],\n",
       "       [423],\n",
       "       [430],\n",
       "       [434],\n",
       "       [441],\n",
       "       [456],\n",
       "       [451],\n",
       "       [445],\n",
       "       [441],\n",
       "       [437],\n",
       "       [424],\n",
       "       [426],\n",
       "       [438],\n",
       "       [443],\n",
       "       [452],\n",
       "       [458],\n",
       "       [456],\n",
       "       [447],\n",
       "       [464],\n",
       "       [466],\n",
       "       [469],\n",
       "       [488],\n",
       "       [492],\n",
       "       [492],\n",
       "       [480],\n",
       "       [460],\n",
       "       [475],\n",
       "       [481],\n",
       "       [496],\n",
       "       [499],\n",
       "       [505],\n",
       "       [501],\n",
       "       [497],\n",
       "       [510],\n",
       "       [501],\n",
       "       [495],\n",
       "       [491],\n",
       "       [499],\n",
       "       [506],\n",
       "       [501],\n",
       "       [492],\n",
       "       [506],\n",
       "       [513],\n",
       "       [506],\n",
       "       [498],\n",
       "       [497],\n",
       "       [510],\n",
       "       [523],\n",
       "       [519],\n",
       "       [526],\n",
       "       [530],\n",
       "       [526],\n",
       "       [523],\n",
       "       [518],\n",
       "       [505],\n",
       "       [515],\n",
       "       [514],\n",
       "       [509],\n",
       "       [505],\n",
       "       [510],\n",
       "       [522],\n",
       "       [528],\n",
       "       [515],\n",
       "       [513],\n",
       "       [507],\n",
       "       [522],\n",
       "       [517],\n",
       "       [522],\n",
       "       [535],\n",
       "       [538],\n",
       "       [533],\n",
       "       [531],\n",
       "       [537],\n",
       "       [533],\n",
       "       [541],\n",
       "       [546],\n",
       "       [544],\n",
       "       [542],\n",
       "       [538],\n",
       "       [542],\n",
       "       [537],\n",
       "       [535],\n",
       "       [532],\n",
       "       [526],\n",
       "       [524],\n",
       "       [527],\n",
       "       [519],\n",
       "       [533],\n",
       "       [530],\n",
       "       [528],\n",
       "       [525],\n",
       "       [519],\n",
       "       [508],\n",
       "       [507],\n",
       "       [501],\n",
       "       [506],\n",
       "       [505],\n",
       "       [506],\n",
       "       [517],\n",
       "       [524],\n",
       "       [534],\n",
       "       [567],\n",
       "       [573],\n",
       "       [579],\n",
       "       [581],\n",
       "       [575],\n",
       "       [583],\n",
       "       [585],\n",
       "       [583],\n",
       "       [582],\n",
       "       [590],\n",
       "       [588],\n",
       "       [594],\n",
       "       [596],\n",
       "       [593],\n",
       "       [592],\n",
       "       [591],\n",
       "       [591],\n",
       "       [590],\n",
       "       [588],\n",
       "       [596],\n",
       "       [596],\n",
       "       [583],\n",
       "       [584],\n",
       "       [582],\n",
       "       [592],\n",
       "       [598],\n",
       "       [600],\n",
       "       [596],\n",
       "       [593],\n",
       "       [594],\n",
       "       [601],\n",
       "       [586],\n",
       "       [588],\n",
       "       [582],\n",
       "       [583],\n",
       "       [583],\n",
       "       [590],\n",
       "       [585],\n",
       "       [585],\n",
       "       [589],\n",
       "       [585],\n",
       "       [594],\n",
       "       [598],\n",
       "       [606],\n",
       "       [612],\n",
       "       [605],\n",
       "       [610],\n",
       "       [606],\n",
       "       [603],\n",
       "       [601],\n",
       "       [603],\n",
       "       [597],\n",
       "       [596],\n",
       "       [597],\n",
       "       [587],\n",
       "       [595],\n",
       "       [602],\n",
       "       [606],\n",
       "       [613],\n",
       "       [612],\n",
       "       [627],\n",
       "       [631],\n",
       "       [639],\n",
       "       [638],\n",
       "       [616],\n",
       "       [609],\n",
       "       [617],\n",
       "       [611],\n",
       "       [604],\n",
       "       [615],\n",
       "       [603],\n",
       "       [601],\n",
       "       [602],\n",
       "       [611],\n",
       "       [613],\n",
       "       [604],\n",
       "       [598],\n",
       "       [607],\n",
       "       [611],\n",
       "       [613],\n",
       "       [620],\n",
       "       [627],\n",
       "       [633],\n",
       "       [633],\n",
       "       [632],\n",
       "       [636],\n",
       "       [637],\n",
       "       [640],\n",
       "       [636],\n",
       "       [636],\n",
       "       [639],\n",
       "       [640],\n",
       "       [639],\n",
       "       [623],\n",
       "       [626],\n",
       "       [636],\n",
       "       [637],\n",
       "       [638],\n",
       "       [642],\n",
       "       [634],\n",
       "       [635],\n",
       "       [634],\n",
       "       [641],\n",
       "       [645],\n",
       "       [648],\n",
       "       [647],\n",
       "       [644],\n",
       "       [640],\n",
       "       [640],\n",
       "       [639],\n",
       "       [647],\n",
       "       [649],\n",
       "       [649],\n",
       "       [655],\n",
       "       [661],\n",
       "       [656],\n",
       "       [662],\n",
       "       [656],\n",
       "       [658],\n",
       "       [653],\n",
       "       [649],\n",
       "       [645],\n",
       "       [655],\n",
       "       [645],\n",
       "       [652],\n",
       "       [653],\n",
       "       [660],\n",
       "       [658],\n",
       "       [652],\n",
       "       [652],\n",
       "       [643],\n",
       "       [632],\n",
       "       [630],\n",
       "       [629],\n",
       "       [635],\n",
       "       [622],\n",
       "       [616],\n",
       "       [624],\n",
       "       [626],\n",
       "       [626],\n",
       "       [615],\n",
       "       [616],\n",
       "       [613],\n",
       "       [615],\n",
       "       [613],\n",
       "       [615],\n",
       "       [625],\n",
       "       [628],\n",
       "       [631],\n",
       "       [637],\n",
       "       [637],\n",
       "       [632],\n",
       "       [635],\n",
       "       [630],\n",
       "       [635],\n",
       "       [638],\n",
       "       [644],\n",
       "       [639],\n",
       "       [635],\n",
       "       [636],\n",
       "       [635],\n",
       "       [631],\n",
       "       [630],\n",
       "       [629],\n",
       "       [640],\n",
       "       [637],\n",
       "       [640],\n",
       "       [645],\n",
       "       [637],\n",
       "       [644],\n",
       "       [636],\n",
       "       [640],\n",
       "       [643],\n",
       "       [647],\n",
       "       [645],\n",
       "       [647],\n",
       "       [645],\n",
       "       [641],\n",
       "       [645],\n",
       "       [642],\n",
       "       [643],\n",
       "       [643],\n",
       "       [640],\n",
       "       [639],\n",
       "       [643],\n",
       "       [641],\n",
       "       [649],\n",
       "       [653],\n",
       "       [653],\n",
       "       [652],\n",
       "       [646],\n",
       "       [649],\n",
       "       [643],\n",
       "       [645],\n",
       "       [649],\n",
       "       [650],\n",
       "       [647],\n",
       "       [653],\n",
       "       [652],\n",
       "       [647],\n",
       "       [644],\n",
       "       [636],\n",
       "       [631],\n",
       "       [617],\n",
       "       [631],\n",
       "       [626],\n",
       "       [636],\n",
       "       [655],\n",
       "       [655],\n",
       "       [653],\n",
       "       [656],\n",
       "       [647],\n",
       "       [636],\n",
       "       [611],\n",
       "       [626],\n",
       "       [607],\n",
       "       [624],\n",
       "       [625],\n",
       "       [630],\n",
       "       [623],\n",
       "       [624],\n",
       "       [639],\n",
       "       [632],\n",
       "       [628],\n",
       "       [621],\n",
       "       [624],\n",
       "       [630],\n",
       "       [634],\n",
       "       [643],\n",
       "       [645],\n",
       "       [642],\n",
       "       [650],\n",
       "       [655],\n",
       "       [657],\n",
       "       [652],\n",
       "       [646],\n",
       "       [648],\n",
       "       [642],\n",
       "       [638],\n",
       "       [637],\n",
       "       [624],\n",
       "       [618],\n",
       "       [616],\n",
       "       [618],\n",
       "       [617],\n",
       "       [637],\n",
       "       [640],\n",
       "       [637],\n",
       "       [645],\n",
       "       [647],\n",
       "       [649],\n",
       "       [650],\n",
       "       [643],\n",
       "       [615],\n",
       "       [632],\n",
       "       [632],\n",
       "       [637],\n",
       "       [641],\n",
       "       [644],\n",
       "       [643],\n",
       "       [645],\n",
       "       [637],\n",
       "       [646],\n",
       "       [650],\n",
       "       [652],\n",
       "       [654],\n",
       "       [652],\n",
       "       [654],\n",
       "       [656],\n",
       "       [653],\n",
       "       [638],\n",
       "       [640],\n",
       "       [637],\n",
       "       [644],\n",
       "       [644],\n",
       "       [643],\n",
       "       [644],\n",
       "       [638],\n",
       "       [626],\n",
       "       [623],\n",
       "       [629],\n",
       "       [619],\n",
       "       [625],\n",
       "       [617],\n",
       "       [627],\n",
       "       [630],\n",
       "       [631],\n",
       "       [635],\n",
       "       [630],\n",
       "       [636],\n",
       "       [635],\n",
       "       [643],\n",
       "       [642],\n",
       "       [643],\n",
       "       [646],\n",
       "       [650],\n",
       "       [651],\n",
       "       [657],\n",
       "       [658],\n",
       "       [657],\n",
       "       [662],\n",
       "       [668],\n",
       "       [673],\n",
       "       [672],\n",
       "       [678],\n",
       "       [678],\n",
       "       [676],\n",
       "       [681],\n",
       "       [684],\n",
       "       [690],\n",
       "       [692],\n",
       "       [694],\n",
       "       [692],\n",
       "       [700],\n",
       "       [702],\n",
       "       [711],\n",
       "       [705],\n",
       "       [687],\n",
       "       [691],\n",
       "       [699],\n",
       "       [701],\n",
       "       [702],\n",
       "       [696],\n",
       "       [698],\n",
       "       [709],\n",
       "       [705],\n",
       "       [718],\n",
       "       [719],\n",
       "       [721],\n",
       "       [724],\n",
       "       [730],\n",
       "       [727],\n",
       "       [734],\n",
       "       [730],\n",
       "       [724],\n",
       "       [723],\n",
       "       [719],\n",
       "       [715],\n",
       "       [712],\n",
       "       [715],\n",
       "       [722],\n",
       "       [720],\n",
       "       [721],\n",
       "       [720],\n",
       "       [727],\n",
       "       [718],\n",
       "       [711],\n",
       "       [718],\n",
       "       [722],\n",
       "       [721],\n",
       "       [723],\n",
       "       [730],\n",
       "       [722],\n",
       "       [730],\n",
       "       [724],\n",
       "       [726],\n",
       "       [721],\n",
       "       [726],\n",
       "       [724],\n",
       "       [723],\n",
       "       [727],\n",
       "       [725],\n",
       "       [728],\n",
       "       [729],\n",
       "       [726],\n",
       "       [727],\n",
       "       [730],\n",
       "       [732],\n",
       "       [734],\n",
       "       [736],\n",
       "       [738],\n",
       "       [737],\n",
       "       [736],\n",
       "       [744],\n",
       "       [738],\n",
       "       [741],\n",
       "       [740],\n",
       "       [746],\n",
       "       [744],\n",
       "       [749],\n",
       "       [766],\n",
       "       [773],\n",
       "       [782],\n",
       "       [777],\n",
       "       [796],\n",
       "       [808],\n",
       "       [807],\n",
       "       [805],\n",
       "       [796],\n",
       "       [811],\n",
       "       [817],\n",
       "       [816],\n",
       "       [817],\n",
       "       [820],\n",
       "       [824],\n",
       "       [828],\n",
       "       [820],\n",
       "       [820],\n",
       "       [821],\n",
       "       [814],\n",
       "       [817],\n",
       "       [817],\n",
       "       [822],\n",
       "       [818],\n",
       "       [809],\n",
       "       [798],\n",
       "       [805],\n",
       "       [812],\n",
       "       [816],\n",
       "       [797],\n",
       "       [807],\n",
       "       [817],\n",
       "       [808],\n",
       "       [799],\n",
       "       [817],\n",
       "       [823],\n",
       "       [812],\n",
       "       [806],\n",
       "       [787],\n",
       "       [783],\n",
       "       [770],\n",
       "       [783],\n",
       "       [788],\n",
       "       [793],\n",
       "       [790],\n",
       "       [801],\n",
       "       [805],\n",
       "       [808],\n",
       "       [807],\n",
       "       [818],\n",
       "       [817],\n",
       "       [819],\n",
       "       [822],\n",
       "       [819],\n",
       "       [822],\n",
       "       [821],\n",
       "       [822],\n",
       "       [817],\n",
       "       [817],\n",
       "       [820],\n",
       "       [819],\n",
       "       [825],\n",
       "       [823],\n",
       "       [829],\n",
       "       [816],\n",
       "       [828],\n",
       "       [838],\n",
       "       [840],\n",
       "       [843],\n",
       "       [850],\n",
       "       [856],\n",
       "       [861],\n",
       "       [860],\n",
       "       [862],\n",
       "       [852],\n",
       "       [845],\n",
       "       [842],\n",
       "       [846],\n",
       "       [852],\n",
       "       [848],\n",
       "       [859],\n",
       "       [849],\n",
       "       [839],\n",
       "       [851],\n",
       "       [854],\n",
       "       [852],\n",
       "       [843],\n",
       "       [843],\n",
       "       [838],\n",
       "       [837],\n",
       "       [840],\n",
       "       [842],\n",
       "       [845],\n",
       "       [832],\n",
       "       [822],\n",
       "       [824],\n",
       "       [826],\n",
       "       [816],\n",
       "       [825],\n",
       "       [823],\n",
       "       [822],\n",
       "       [819],\n",
       "       [814],\n",
       "       [810],\n",
       "       [821],\n",
       "       [819],\n",
       "       [826],\n",
       "       [828],\n",
       "       [820],\n",
       "       [829],\n",
       "       [829],\n",
       "       [842],\n",
       "       [853],\n",
       "       [856],\n",
       "       [858],\n",
       "       [868],\n",
       "       [876],\n",
       "       [884],\n",
       "       [877],\n",
       "       [875],\n",
       "       [875],\n",
       "       [875],\n",
       "       [872],\n",
       "       [875]], dtype=int64)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "训练集的形状是： (639, 1)\n",
      "测试集的形状是： (117, 1)\n"
     ]
    }
   ],
   "source": [
    "print('训练集的形状是：', Train.shape)\n",
    "print('测试集的形状是：', Test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAxYAAAE6CAYAAACLT8TDAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAACSjElEQVR4nOzdd3hUxdfA8e/dlp4s6YSEQOhICyCEjnQFG3ZUsGBB7OKLiqjYsFcUu/hTRAQrIiAIUqRJCV0JJQkhkJDes9nd+/4RsmSzm14J5/M8PLJ3bpk7Ces9d+bMKBkZGSpCCCGEEEIIUQuaxq6AEEIIIYQQ4vwngYUQQgghhBCi1iSwEEIIIYQQQtSaBBZCCCGEEEKIWpPAQgghhBBCCFFrElgIIYQQQgghak0CCyFEs6OqKl26dCE0NJTCwsLGro5TX375JUajkTfffLNBrxsXF4fRaGTu3LkNet3qmDt3Lkajkbi4uMauioN///2XG2+8kdatWxMWFsbNN9/MsWPHGrtaQgjRJEhgIYRodnbv3s2pU6fIyclh48aNjV0dp1asWGH339rIyMhg7ty5VbpXPz8/PvjgA8aPH1/r61bHxo0bmTt3LhkZGZXuO378eD744AP8/Pzqv2LVsHfvXkaPHk10dDQPP/wwDz/8MDt27GDMmDGcPHmysatnM3fuXH777bfGroYQ4gIkgYUQotkp/bC+cuXKRqyJc3l5eWzYsAGAnTt3kpycXKvzZWZm8uqrr7Jp06ZK9/X09OTmm2+mR48etbpmdW3atIlXX32VzMzMSvft0aMHN998M56eng1Qs6p76KGHKCoq4vfff+fRRx/lscce47vvviMlJYU33nijsatn8+qrr7J8+fLGroYQ4gIkgYUQotlZsWIFnTp1YtCgQU0ysFi7di0FBQXceeedqKraJOso7O3bt4/du3dzxRVXEBERYdseGRlJ69atWbt2bSPWTgghmgYJLIQQzUpCQgL79+9n6NChDB06lISEBPbu3Wu3T/fu3Rk/fjwffvgh3bp1IzAwkOHDh7NmzRq7/caPH0/37t1ZunQpffr0ITAwkP79+7N48eJa1XHlypUoisIjjzyCq6ur08BCVVU+//xzoqKiCAoKokePHsyYMYPU1FS7+hmNRnr27AkUv6k2Go0YjcZyhzpVlGPx33//YTQaef311x3KBg0axIABA+y2RUdHc8MNN9CuXTvCwsK45JJLWLZsmcO1jEYjr776KgA9e/a0bVu4cKHTOlaWY2E2m3nrrbeIjIwkICCAyMhI3n77bSwWi22fhQsXYjQa2bhxI9999x39+/cnODiYqKioGgVyW7duBaB///4OZddee63D9l9//ZVLLrmEoKAgOnTowCOPPEJaWpqtvKKfQ0REBNOmTXPYb+fOnVx66aWEhITQrVs3u5/TtGnTbO0KsGjRItvn7t27V/t+hRCiJnSNXQEhhKhLJcOghgwZgr+/P3PnzmXFihUOQ3927tzJ3r17mTZtGm5ubnz55Zdcf/31LF68mNGjR9v2S0pK4v7772fq1KkEBwfz3Xffcc8995CXl8ftt99e7fqpqsoff/xhSy7v168ff/31FwUFBbi6utr2e/TRR/nyyy8ZMWIEd9xxBydOnOCTTz5hw4YNbNiwAVdXVx555BEmTZpEWloas2fP5rLLLrMFFEFBQdWuW6dOnYiMjGTp0qU8/vjjtu3//vsvBw4cYM6cObZtMTExXH755bRo0YKHHnoIV1dXfvrpJ6ZMmcKKFSvo37+/LZ8DYPny5fz++++88MIL+Pr6AjgEKlVtvylTprB8+XLGjx/PPffcw6ZNm5gzZw7R0dEsWLAARVFs+y9YsIA///yT++67D41Gw7vvvsvtt9/Orl27aNmyZZWvGxsbC0CrVq0cyp555hm7zx988AGzZs2iR48ezJ49mxMnTvDFF1+wYcMG1qxZQ4sWLap933v37uXDDz/khhtu4Morr+Szzz7jpZdeokuXLkyYMIHJkyczePBgAKZPn05UVBS33norQJMbUiaEaL4ksBBCNCsrV65Eo9EwZMgQPDw8cHd3Z+XKlcycOdNuv4KCAhYvXsywYcMAuO666+jduzcvvfSSXWBhMpl4//33bQ9pt912G7169eLll19mypQpaDTV6/jdsWMHycnJTJw4ESgOgDZs2MD69esZO3YsANu3b+fLL7/kqquuYsGCBbZjPTw8eOWVV/jzzz8ZP348o0aNAorfas+ePZvu3btz8803V6/ByrjxxhuZOXMme/futQVjS5YsQaPRcN1119n227t3LwMHDuSpp56y9ZhcddVVdOzYkd9//53+/fvb8jkA4uPj+f3337niiisIDw+vcf1++eUXli9fzl133WV7Y3/vvffy+OOP8+mnn/Lrr79y5ZVX2vb/7bffWLduHV27dgXAzc2NWbNmsX37drv9KpOTk2M7viKJiYnMmTOHHj168Oeff6LX6wEYOHAgU6ZM4eWXX3baI1SZFStW8N577zF58mSgOCgbNmwY69evZ8KECQwYMMAWqE2fPp22bdvW+ndBCCGqS4ZCCSGajZycHDZt2kTHjh0xmUykp6fTo0cPoqOjOXXqlN2+rVu3tgUVAKGhoURFRbFnzx7y8/Nt23U6HTfddJPts6enJ+PGjePMmTMcPXq02nUs6VHp1q0bSUlJdOnSxW47wKpVqwCYOnWq3bEzZ84kJSWFyy67rNrXraprr70WvV7PDz/8YNv2ww8/MGzYMEJCQmzbrrnmGhYvXkxERAQbNmxg/vz53HbbbQCcOXOm3ur3+++/A8UPz6WVfC4pL3HbbbfZggoobnc4FyhUVUkAWXq4VYljx46xf/9+ANasWYPJZOLuu++2BRUAV155JaGhoTWeBaxbt2624LbkM1T/PoQQoj5JYCGEaDb+/PNPCgsL+ffff+nUqROdOnVi69atqKpqe1gv4WyoUEBAAKqqkp2dbdvm5+eHTqdz2A+o0gxHZZWM77///vvp1KkTt9xyC1AcTKiqCpx7MC/9IA+gKAo6nc5uqE9d8/PzY/To0fzwww+oqso///xDbGysXXAFcOrUKSZNmkSbNm2YOHEi//vf/7jooosAbPdRH0pm0Co7JKnkc9kZtsoOt6puD1OJkqlvnQVNd999ty3YK69+JduSkpIqvI7VaiU3N9dhe1RUlN3PXavVVr3yQgjRQCSwEEI0GyVvg1999VWWLl3K0qVL+fDDD+3KSiQmJjocf/r0aTQaDd7e3rZtKSkpmEwmh/0AW65AVcXGxnLw4EH69Oljq9/SpUsZPnw4p06dIjo6GjgXuJSt46lTp7j77rv55ptvqnXd6rrxxhtJSEhgy5YtLFmyBC8vLyZMmGC3z/3338+6detYuHChbd+aDPGprsDAQACHdSNKPpe0XYm6WgujV69eALafUWnx8fG2QKK8+pVsKykvz5EjR5wu6tjU1vQQQghnJLAQQjQLVquV1atXYzQaufPOOxk1ahSjRo1i0qRJhIeHs379evLy8mz7nzx50m4WqNjYWLZt20afPn3skqgtFovdg3xWVhYrV64kODiYtm3bVquOJb0VV199ta1+o0aN4vrrrwfOBT8lOR5ffPGF3fFr1qzh+++/t+tRgeKHTkVRyMrKqlZ9yjNu3DhatGjBokWL+Pnnn7n88stxd3e322fbtm1cdNFFjBs3ztZeZetbmr+/P0Ct63jppZcC8Mknn9ht//jjjwHqbeG/UaNG4e/vz+LFi+16LdauXUtycrJtWN3IkSPR6/V88cUXmM1m237Lly8nISHB1rNRklBdEqSWqIv1MPz8/Orsd0EIIapDkreFEM3Ctm3bSE1N5dprr3UYujRmzBg+/fRT1q1bZ3vw9Pb25vbbb7fNCvX5559jtVp56qmn7I51c3Pj6aef5ujRowQHB7No0SLS09N5//33qz0kqSRwGDdunEP9NBoNK1as4KmnnrLN6PP111+TmZnJ2LFjiY+P54svvqBdu3a24VMlPD09GTx4MAsXLiQoKAij0ciBAwcYOnQol19+ebXqCGAwGJg4cSKff/45UNyDUVb37t3Ztm0bc+bMoWXLlqxdu9a2lkNBQYHD/iNGjMDFxYUZM2Zw4403kp+fz9q1a1m6dGm16nbllVcybtw4PvzwQ06ePMnAgQPZvHkzv/zyC+PHj+eKK66o9v1Whaurqy15evTo0UydOpWMjAzmz59PQEAADz74IFA83OmZZ55h9uzZjB49muuuu842K1Tbtm154okngOKH/65du/LDDz9w+eWX07lzZz7++GN27NiBh4dHreo6btw4lixZwksvvURoaChHjhyhbdu23HHHHbVuByGEqIj0WAghmoWS3oCSmZVKK3nLXXo4VPfu3XnppZf47rvvmDt3Lv7+/ixdupRLLrnE7lh/f38+//xz1qxZw/PPP4/FYuHTTz+1S6StiqysLDZv3ky7du1o3769wzX69u3Lvn37SEhIAOC9997jtddeIyEhgdmzZ/Pbb79x66238ueff+Ll5eVw/s8++4yxY8fy/vvvM2PGDNasWYOPj0+16lhaSU5FaGgoQ4YMcSj/5JNPuOyyy/jiiy+YM2cOBQUF/Pbbb4SGhvL333879Kq0a9eORYsWYTKZePLJJ3nxxRcd9qkKjUbD119/zdNPP82ePXts/509ezZfffVVveafXHbZZSxfvpw2bdowd+5cPvnkE0aNGsXq1avt8mEeeOABFixYgKqqPPfccyxZsoQbb7yR1atX2w1pWrBgAb169WLKlCkMGzaM1NRUVq1ahYuLS63q+corr9gC00cffZQff/zRbnifEELUFyUjI6P+suyEEKIJ6t69O61bt2b58uUV7jd+/Hji4+PZt29fA9VMCCGEOH9Jj4UQQgghhBCi1iSwEEIIIYQQQtSaDIUSQgghhBBC1Jr0WAghhBBCCCFqTQILIYQQQgghRK1JYCGEEEIIIYSoNQkshBBCCCGEELUmgcUFICYmprGrcMGQtm440tYNS9q74UhbNwxp54Yl7d1wGrOtJbAQQgghhBBC1JoEFkIIIYQQQohak8BCCCGEEEIIUWsSWAghhBBCCCFqTdfYFahMbm4uZrO5satxXnN1dSUzM7Oxq3FBkLZuOPXZ1h4eHuh0Tf7rUQghhGhSmvT/OQsLCwHw8fFp5Jqc31xcXHB1dW3salwQpK0bTn21taqqZGRk4OXlJcGFEEKIZkvJSEUTdwRraBtUv6A6OWeT/r9mQUEB3t7ejV0NIcQFRFEUjEYjWVlZ8lJDCCFEs6Q9uAvX92aj5OeiKgrmoeMpvO0R0Ghrdd4mn2OhKEpjV0EIcYGR7x0hhBDNlXIyFtf3i4MKAEVV0a//DW301lqfu8kHFkIIIYQQQojaU04n4PbWEyh5uQ5l2qMHa33+Jj0USgghhBBCCFF7Sspp3J+/DyU3y2m5Jimh1teQHgvRrKSkpPDuu+82djVEE7Rz505++umnxq6GEEII0eB0f/2Gx2M3lhtUAChJJ2t9HQksRLNhtVqZPHkyISEhjV0V0QSFhYXx8ssvs3///sauihBCCNFgdFv+xPXLNxy2W9p2tvusjT8CVkutriWBRR1buHAhRqPR4c/GjRtrfe64uDiMRmONjjUajcTFxdW6Dk3Z//73P9q3b891110HYNf+Xbp04cUXX6zW+T7++GM6dOhASEgITz31VH1UuVLJycnccMMNBAcH07NnT3799dd6u1Ztfr9qKi0tjVtuuYXWrVsTFRVl9+/kxIkTTJgwgeDgYAYNGkR0dLTdcXfccUe1jgsMDOT999/n8ccfb6jbE0IIIcBqbbxrqyr6ZV87bvbwIv/peaiu7nbb3R+7CUyFNb6cBBZ17LrrriM2NpZNmzYBEB0dTWxsLAMGDKj1ucPCwoiNja3RsbGxsYSFhdW6DvVp7ty5TJs2rUbHqqrKe++9xzPPPGO3fdmyZcTGxvLtt9/y9ddf8/PPP1fpfFlZWTzxxBN8+eWX7Nixg0mTJtWoXrV17733kp6ezqZNm5g6dSp33nknx44dq9KxtWnPhjJnzhyysrLYuHEjQ4cO5Y477qCgoACA6dOn4+npyc6dOxk8eDCTJ0/GevbLuabHRUVF4e/vz4YNGxrnhoUQQlw4VBWXz1/DY+oY3F5+CLIyGrwK2kO70Z6MddheNORS0OmwBtqP8tCkJaPbsqbG15PAoo4ZDAaMRiNeXl4AeHl5YTQa62ShLY1GU6seC42m+f64d+3aRceOHfH397fb7unpidFoJDIykgEDBrBr164qnS89PR1VVRk8eDAhISF069atPqpdocTERNauXcuzzz5L+/bteeCBBwgNDWXp0qUNXpf6snr1aqZNm0Z4eDh33HEHZ86c4fDhw8THx7NhwwaeeOIJWrVqxZNPPklCQgJ///237bi777672scBXHvttSxbtqyxblkIIcQFQrtjPfoNv6NYzGj/24N+XS1HHagq+l/+h9szd2FY+hmoauV1iN7ieBp3T4rGXAuANdjxpbP28L4aV/G8e9I0fnmyQf/UtZK3yOvWrWPo0KF2Q2y2bdvGkCFDaNmyJSNGjODff/+1O9bZUJWSbf/88w/9+vUjLCyMOXPmOLabk6FQRqORtWvXcskll9CqVSvuuece1LO/pEVFRdx7772EhYUxceJEbr/9dkaPHl3p/WVkZDBp0iTCwsLo3Lkzn376qa0sJyeHBx98kIiICC666CI+++wzABYvXozRaOTVV19l0aJFGI1GunfvXum1Sjtw4AB9+vQpt/zkyZNs376diy66yHZ/zz33HJ07d6ZDhw52w6T8/Pzo2bOnrY1KD2UrKChgxowZRERE0LdvX7uhSRs3bqRv377ExsZyzTXXMHLkyErvvbJ7Amx1BujUqROHDh0C7IcDRUZG2gKOytozIyODO++8k/DwcIYMGcLmzZvtrvvGG28QGhrKwIED7QKxM2fOMHnyZFq3bk3v3r3t7n3hwoWMHz+e6Ohoxo0bx+TJkyu9PyjuXYiMjLTdD4Crqytbt27FxcWFHj16AMU/h7Zt27Jt2zbbcSU/o+ocB9CnTx/JsxBCCFHv3OY9Z/dZv2V1rc6n3bcdlx+/QBsXg2HZN+g2V34+TYL9KAdz36HkvfI/VL9AAKwdHZ+3tMcO1biO511g0RwcOnSIWbNm8dhjjzF16lSgeCjPbbfdxoQJE4iOjmbgwIHMnj27yuecOXMm8+fPZ/78+bzzzjtVHjI1c+ZMZs+ezU8//cQvv/xiGyLy7bffcvjwYf7++29cXV3x8PDgm2++qfR87777LikpKWzevJlPP/2UJ598klOnTgHw1FNP4eLiwtq1a/n444958cUX2bt3LxMnTiQ2NpaHH36Ya6+9ltjYWLs3zFWRkpJCQECAw/bLL7+c1q1b061bN/r27cv1119vq+exY8dYtmwZ33//PUuWLOH3338H4NixY7ahbLGxsXZD2WbPnk10dDQrV67kmWee4Z577rEb+19YWMjNN9/MZZddxhtvnEuUKu/eK5Keng5g6/0C8Pb2JiMjAygeJlVQUMDGjRt5/vnnmTZtGsePH6+0Pe+55x5yc3NZv349d9xxB9dffz2JiYm28t27d7Np0ybGjx/PrbfeSmFh8VjLu+++mx49erBp0yZefPFFpk+fTlJSku24M2fOcM899zBlyhSeeOKJCu+txPTp0wkODgZg3rx59O3bl44dO5KUlOTQy+br62u73vTp0wkKCqr2cQABAQGkpKRUqX5CCCFEjRQWONlW89wFAP365XafXT95udL8DU3CcfsqXHMnqo+v7XPR0Esx9xlit49yKh7ycmpUR1nHohEcPHiQ7du306ZNG9s2VVVZs2YNAQEBHDp0iLy8PGJiYqp8zscff9z2xj4oKIiEhAS785dn6tSpjBgxAoAePXpw8mRxL83evXsZPnw4rVu3ZvTo0Sxbtsz2IFcRg8GA2WzGarUyZMgQ2wOc1Wpl8eLFGAwGFi9eDEBubi7bt2+nR48eGI1GXFxc0Ov1NRru5e7uTk6O4z+CTz/9lC5durBr1y4eeughvv76ayZPnsy3335LUlISf/31FwD5+fls3bqVyy67DB8fH9vDe+m6WK1WvvnmG3744Qc6duxIx44dWbVqFV999RW9evUCih+uX3rpJVsAU5V7r4nTp0/zxx9/cPDgQUJCQggPD2fBggV2beisPU+fPs2qVavYv38/oaGh3H777fz4448sXryYiRMnAvDcc8/Rpk0b/u///o+PPvqIHTt20LZtW9atW8fOnTt57733gOJemD179jBmzBgADh8+zMqVK4mKiqr2/cyaNYsdO3bwxx9/2LapZbp4rU6+PGtyXG5uLh4eHtWuoxBCCFFV2oOOQ6+tIeHVPo+Slow25gCWTj3QOMmVcP+/W8h75kPwNjoenJ2BJjPN9lHV6VEDW9nv4+JGwYMv4PbUbbZcDEVV0R7/F8tFfatdXwksGsG4ceMcHvo1Gg2ff/45CxYsICQkhNatW2OxVH3Kr0GDBtn+rtfrHR6uqntc27ZtWb16NRaLhX/++YeOHTtW6Xz33XcfycnJXHrppWg0Gm655RaeeOIJUlJSKCws5Ndff7W9oQZqFEQ4Exoayvr16x22BwcH06ZNG9q0acOWLVv49NNPmTx5MomJicyfP5/evXvb9i3dM+BMamoq+fn5dj+7Nm3asH37dttnPz8/rr32Wrvjanrvvr7FbxQyMzNtf8/KyqJFixacPHkSnU5nN7Xu+PHjKzwfYDsuNDTU7h4SEs4tihMeXvzFp9frCQwMJCkpCRcXFzQaDRs2bEBRFNu+pXNaevXqVaOg4qmnnuKnn37it99+s7VtcHAwGRkZWK1WW+9Denq6Xfs9++yz/Prrr9U+Li4uzu7+hRBCiLqmiXN8OawU5lfrHMqZU7jNmYYmOwNVrwet3vE6ZxJxf/kB8uZ8Ai5u9mVlAhFrSGsoJ+fX2q6rXZK3Ju7IhRFYZNzeqvKdmjhnb0s3bdrERx99xM6dO2nZsiV//PEHjz32WJXP6e3tXaO6lPcw3bFjR1577TWCgoJo3759lRcWO378OE8++SRvv/02e/fuZfz48QwYMIAhQ4bg4uJCQUGB7cH1448/JiIiwpa7odFoqhwQlTVkyBCeffbZCvdRVRWz2QxASEgI2dnZtrosWbIEq9XKDTfcUO7xfn5+uLq6Ehsba3ugj42NtXtIdXNzc0iS9/f3r/TenSnJrThw4ABDhhR3Ux46dIhbb72VVq1aYTabSUxMtNXl3nvvZeTIkbbpdp21Z8lxCQkJtnrHxsbaeq0AEhISiIiIwGKxcObMGQICAggJCcFqtaKqqu0hfu7cuYwZM8bWU1aTXoCFCxfy448/snLlSruAbcCAARQVFbFnzx4iIyNJT0/n+PHjtiFpCxcu5Oeff2bVqlXVOg5gxYoVjBo1qtp1FUIIIapKk+hkin9nw6Mq4PK/d9BkZwCgFBVBUZHza506gcfD15H38gLUFude+GnLDIOytmpb7rXKlmlOn6hWXW3H1egoUeeys7NRFIWcnBy2bdvG008/XeOH7Lrw9ttv8+KLL7J582bWrVtHy5Ytq3TcvHnzmDlzJkePHrUNQSl5e3z99dfzwgsvcPjwYdavX8/zzz9v99a+ffv27Ny5k5MnT3LgwIFqrbvh4+ND7969+f777+225+TkkJqayp9//smSJUtsPTQ33XQT77zzDtHR0URHRzNr1iwMBkOF1yjpgXnmmWeIiYlh2bJl/Pjjj0yZMqXS4yq7d2dKkviff/55jhw5wrvvvktiYiITJ04kODiYUaNGMWPGDOLi4li5ciW//vqrXaK3s/YMDg5mzJgxzJgxg9jYWL788kt27dplN3Rrzpw5xMfH88Ybb+Dm5kafPn0ICQlh2LBhPPnkk8TGxvLDDz/w/vvvO81rqar8/HxeeuklXnrpJYxGIxkZGWRkZGAymQgNDWXo0KG88sornDx5krlz5xIeHk5UVJTtuDlz5lTrOCju/fnhhx+48sora1xvIYQQoiLaPVvRb1vrsF2pamBRWIDb89PQ7d1W+b4l587LQff3KrttZXtNKgwsWtrPDqU5JYHFeW3UqFGMHTuW4cOH88gjj3Drrbdy+vRpkpOTG6U+l19+OU8//TTDhw+nZcuW9OrVi507d1Z63Jw5c8jNzWX48OFcffXV3HrrrQwfPhyAl19+mc6dOzNu3DimT5/OK6+8wsUXX2w79qqrrqJPnz7069ePyy+/3C6huCqee+45Xn31VVueSMl9tGvXjvvuu49rrrnGts7Fww8/zIQJE7jhhhu4/vrrufvuu7n66qsrvcbzzz9Pjx49GDNmDM899xzz58+35VdUpLJ7L89HH32Er68vgwYN4vPPP+ezzz4jIiLCVmYwGBgyZAhPP/008+fPp2vXrrZjy2vPjz76CDc3N4YOHcpnn33G4sWLadXqXE9g+/btGTZsGD///DMLFizA3b148ZxPP/0UvV7P0KFDmTt3Ll988QWtW7eu9B7Ks3PnThITE7nzzjttw9XatGnDkiVLAPjggw/Izc2ld+/e/P333yxYsACNRmM77t57763WcQD/93//x1133UWLFi1qXG8hhBCiPJr4I7i+Xc6ius4CC7MZw9fv4nH/lXhMm4DLRy9i+PELtEerPzNT2URtzdGDdp+tEZ3KPbbstLPaw3vtp7QtMpV/X6UoGRkZjfdavBKZmZn4+Pg0djXOewUFBbi6ulZ5/+zsbLp3787SpUuJiIggNzeX2bNnExoaWu3VqxvaypUr+f777/niiy8a5frVbWtRc9Vt61WrVrFo0SK+/PJLuzyR8sj3j72YmBg6dOjQ2NW4IEhbNwxp54Zl196qClX4Hj4fuXz5Jvq/nK+VpLp7kDt/eZX3r4i5R39U7xboN620bbO07UT+cx8Xt3VoCB7TJqCcDQxURSH3w2Xg7un8hBYzHneNQ7GY7TbnPzIX7eG96P/8BaUgj5yv/qqwXuddjoWof15eXtx6661MmTKF5ORkPDw8GDx4cJNfxRmKE+MvueSSxq6GaIJKegSrElQIIYSoH7r1y3H5/mNUDy8K7p2NNaJzY1ep7pgK0W3+o/zygny7oEpz5ECNggoAa0BLii6/xS6w0Jw6Yeth0B7/zxZUAKgtW5cfVABodaA3QJnAwvWdWShqxVPaliaBhXDqhRde4IUXXmjsatSIi4tLY1dBNFHyuyGEEI1DMRdhWPoZhmXFa2IpOVm4vvc0eS8vqPiB9zyi3bMVxVT+WhWK1QrmouIHeEC3d3u5+1bGGtYO1eiH6uqGUlA825RSkIdydnpZTZlF7iztujqcoyxLh27o9tnXqTpBBUiOhRBCCCGEqGftvnvfFlSU0KSn4PL9J41Uo7qn27Gh8p1K5VloEmMr3FV1daPokisctysKlsiBoChYg8omXccDxT0WpVmq0DNk6V557mdlJLAQQgghhBD1RklNwivWeTKyft2vaP7dU/whN7v4jf75yFSILnqz3aaCabOxehnttpWeGUpxNiVtKUWDxlIUNdJhu7VDN1SjX/Hfy8zmpCTG4ZYY6xDkWNuUn7htu97wCRQNqN107BJYCCGEEEKIeqOkJlVY7vL1uxh++hKPB67G46FrzgUa5xHDD5/bhiQBWH1aYO43HDzKrBdWskiexYzmdAIVsXS7GGunHhQNv9xue9HAc2tglV3N2+W7j+j8xUt221StFmto+VPNnjvYjcJ7n8bce3Dl+5ZDAgshhBBCCFFvNOkpFZZrE45h+PkrFIsZJScLl+8/aqCa1Q0lKQH9qiV228x9h4FGi+piP3uhUlicwK3bsdFhBqayrO26gKJQeOtDmC67CWtIOKbR12AeNr7UdYban9/kOKWtNSgMDFXPMTT3GeKwTdXryZ37VaXHVimw2LFjB4MHD6Zly5Zcf/31pKenA7B//36GDRtGcHAw48aNIzY21nZMRWVCCCGEEOLCoKSn2n02jbyqwiE32qOHoIIk6KZGe3CX/QxMegNFE24u/lA2sMjLwW3ONFw/nFPpeVUf3+K/6HSYbriHvLlfYbrlAdBoz+3Tqg3mi/pUeB5r+8oTt0szDxpD4VW3oZ6dvUo1uFJw9yzUMr0jzlQaWJjNZm6//Xb69evH1q1bKSgo4PHHH8dqtTJ58mQiIyPZtWsXQUFB3HfffcU3UEGZuLBs3bqV3377rbGrIQRfffUVMTExle8ohBCiTikZ9j0WqtEP01VTUDXlP4Zq4o/Ud7XqjJKZbve5aOhlqL4BAA49Fvp1y9Ae/7fSc1q9q76Qq7ME79IsXSsOPBwoCkVX30beq19TcM8s8t5chKXf8CodWmlgceTIEU6cOMHtt99OeHg4Dz74IMuWLWPz5s0cP36c2bNnExISwowZM9i8eTNxcXEVlokLR2JiItOnT6djx46NXRUhaN++PVOmTCEnJ6exqyKEEBcUpcxQKLWFP2pwGObB48o9RhN3/rwI0pyd4rWEWnoVaxc3uzLt3m0Ox6uubhTcPsNum+mWB6p8fUuP/qjlDHWydOyOuf/wKp/Lrl5BoZgHjkatRpBTaWCRn1+cZFIy/7uvry+FhYVs3bqVtm3b4udXnJXerVs3XFxc2LZtW4Vlzd3ChQsxGo0OfzZu3NjYVWtwzzzzDDNnzrQFFmlpadxyyy20bt2aqKgouzY5ceIEEyZMIDg4mEGDBhEdHW0rq+lxFTl69CiXXnopQUFBDBgwgM2bN1d+UA1t3LiR7t2719v565PRaKz0hUBTvr+4uDiMRiMAgwYN4pprruGdd95p1DoJIcSFRuPQY+EPgOnKyeUeo409XK91qrH8PLR7tqGknbFtUsoGFj7nHsTVMoFF6VmhACxtOpL34heYh0+g4O6nMPcZQuGk6ZgvHl71Orm4YunR33l1H33FbuhUfas0sIiIiMDd3Z1Vq1ZhtVr57rvvAPjrr79sgQOAoigYjUaSkpJISkoqt6y5u+6664iNjWXTpk0AREdHExsby4ABA2p97vHjx7Nw4cJan6chrnf06FGOHj3K9ddfb9s2Z84csrKy2LhxI0OHDuWOO+6goKD4H9j06dPx9PRk586dDB48mMmTJ2O1Wmt1XHksFguTJk3Cz8+PrVu3Mnz4cG666SYyMjKqdG/Tpk1j7ty5NWgV0dimT5/Ojz/+SG5ubmNXRQghLhjOeiwAVP/gcoMLTZl1GJqEwgLcXrgPt7dm4j7jRjRHDwKOQ6GsJbkR4JBjUZqqKOQ/8TZqQEugOLeh4MEXKBp7HVQwTMwZc/d+jud3dQc3j2qdp7YqXXnbx8eHuXPn8vjjjzN37lxuv/12ADw8PGwPdyVKP9CppZJYypY542zss6ur63m5Uq6rqysGQ/Gqinq9HldXV8xmM2Zzxdn/lbFarRQVFTm0e1XU5JjaXO+nn37iyiuvtDv2jz/+4NVXXyUoKIibb76ZTz75hP379+Pj48OGDRv4448/8PPz45FHHuHTTz9l3bp1DBo0qMbHlWf79u38999/LFy4kJYtW/Lkk0+yYMECli1bxnXXXVfpvVksFsxmc7ntUna7yWRCVdUatWNTUFhYWGHdG/P+KrtmYWGhw37Dhw9nxYoVTJgwocJjs7KySE5Orn0lmxHJUWk40tYNQ9q5AagqPUq93Qc4kpaJJb+47ZUuUXTYsQmPk8fs9tEkHOPogX1YDeU/mDe0gO1r8DwZC4BisVDwwwLirr6LrilJlO4TiE3PovDs71ZIXj5B5ZyvwL8lMQmJdVI3N60bZZfAK/TwrvPf8Q4dOlRYXmlgATBlyhRuuOEGioqK2Lt3Lx988AH9+vXjm2/OraBotVrJzMwkODgYk8lEamqq07LqVDQzMxNXV/tfKM8pw6tS5TqT89VfNTquJCBydXW1u4fjx4/z6KOPsmPHDsLCwnj99ddtD8F79uzhkUce4dChQ4SHh/Pmm28yaNAgHnjgAb7++msAtmzZwsMPP8xNN93E/PnzK63Hnj17eOihh/jvv//szllRXWpzvRKHDx/m9ttvt7v3+++/n379+uHq6mp7Y+zt7c3u3btxcXGhb9++aDQagoODadu2Lbt372bkyJE1Pq48MTExeHt70759e9vPqG3bthw5cgRXV1fi4+N55JFH2LZtG61bt+bll19m+PDhvP7667z00rm5oUvacvny5QAkJCTwwAMPsHPnTrp27crbb79Nly5dbEHmrFmzWLRoEd27d2f+/PlERERU+HMAmDt3LvHx8Vx//fU8++yzDB48mJdffrnS9t+2bRszZszgyJEjdOnShQ8//JDOnTsTFxdHz549Wb16NdOnT+fUqVNMnTqVZ599FiieAe7BBx/kxIkTPPzww0Dx73LZf4el1fT+vvvuO9555x22bt0KFD/8t2vXjuXLl9OrVy8OHjzIo48+ysGDB+nfvz/vvPMOrVq1YuPGjdx33328+OKLzJo1i5ycHJ566inuvvtuAFauXMmTTz5JRkaGbVvp+vfr14///vuPa6+9tsI29Pb2JiwsrMJ9LiQxMTGV/g9F1A1p64Yh7dxAcrPRFplsH1WDCxHde8LZGYcA1Gc/pGD337jOf8G2TVFVOmgtWJvQz8jjtfvtPvse2I7h/17DkJdtt711j0jb+hWG/S3LPZ+uS6+6+x1s2wY+s9+kd/do8N/xSvtZcnJyuOaaa8jOzsbLy4tdu3bRrl07Bg4cSGxsLCkpxd1b+/btw2QyERUVVWHZhcxsNjNp0iQmTpzI5s2bufvuu7nzzjttb1VnzJhB37592bVrF5MmTbI92L322mvExsYSFRXFG2+8QWxsLG+++WaVrjljxgz69OnjcM6K6lKb65VISUkhICDAbtv06dNtweW8efPo27cvHTt2JCkpCaPRiKZUt5+vr69t6FxNjytPeno6Xl72C9b4+PiQkZGBxWLhpptuIjw8nK1bt3LbbbcxefJksrOzefDBB4mNjeXaa6/l4YcfJjY2lsWLFwPFwfONN95IaGgomzZtYujQoVxzzTW2QCghIQFFUdiyZQsdO3Zk6tSplf4cShw6dIhZs2bx2GOP2Y6riKqq3HbbbUyYMIHo6GgGDhzI7Nmz7faZOXMm8+fPZ/78+bzzzjvExsZSUFDArbfeyvjx49m4cSO7d++u9FolanJ/48ePJzY2lsOHi8fRrlu3jqCgIHr16kVOTg4TJ05k+PDhbNy4kZYtWzJp0iRbz2d6ejrz5s1jyZIlPPXUUzz99NMUFBSQnJzMHXfcwYMPPsjatWtZt26dQ10DAgJs301CCCHqlybVvudXbeFvF1QA4OKKOWqk3cJvcHba2aYiJwvF2RS4+Xl2a0eoOj24e54rz812POYscy1Xubaj0ztsUrLSnOxYvyrtsfD09OTkyZN88sknXH/99Xz++efcfvvtREVFERERwQsvvMDMmTN58803GTx4MGFhYYSGhpZbdiHbsWMH//77L7NmzbJty8rK4vjx43Tu3BmDwYDJZMLNzY0HH3yQBx98EAA3Nzfc3NzQarW4ubnZklGrorxzVlaXml6vhLu7e7mz78yaNYsdO3bwxx9/2LZVZehcTY+ripLz7Ny5k9jYWNauXYuLiwtTp07F398fi8WCi4sLLi4u6PV6XFxc7Nplx44dHD9+nN9++w2j0ciTTz7J999/z8qVKwkMDESv1/Pcc8/h6enJ7Nmz6dixIydOnODkyZMV/hwADh48yPbt22nTpk2V72XNmjUEBARw6NAh8vLyHLpCH3/8cfr0KZ5+LigoiISEBBISEsjJyWHmzJnodDqeffbZKk8VXNP7GzVqFL/88guPP/44y5cvZ+LEiUBxr4ObmxtPPPEEAK+++irt2rVj586dQPELj9dee40uXbrQrl07/u///o/k5GQ2bdpEaGiobcjmzJkzueaaa+zqmpOTg4dHw445FUKIC5WScsrus9W//Df41ogusHm17bP2yAGK6q1m1aPfssbpds0J+2lxVR9f+96YwBCnx5k798LSJbLuKghYQtuiTTh+7nOX3nV6/qqoUmbIxx9/zG+//cYll1zCiBEjuO+++1AUha+++oro6GgiIyNJTk5m3rx5ABWWXcgSExNtQzlK/uzZs4e2bYuXWX/rrbdISkqiZ8+eDBo0iGXLltX6mm+99RbJyckO56ysLrUVGhrqdDahp556ih9//JHffvvN9qAcHBxMRkaGXVCQnp5uN3Supsc54+vrS2Zmpt22rKwsWrRowcmTJwkKCrINZdNoNEycOLHS4KrkuJIhN4qiEB4eTkJCAgB+fn54eha/wSgJNJKSkqr0cxg3blyVg4qSOn/++ed07tyZ6dOnc/r0aSwWi90+pXNQ9Ho9qqqSlJREYGAgOl3x+4bWrVtX+Zo1vb+JEyfy66+/YrVaWblypS0IOHnyJOHh5xbicXNzswVAUDxbVZcuXYBzQ7FK7qFVq1a245zdQ3x8PKGhoVW+NyGEEBVbEZ9P76WnGfpLMntSTXZlmjP2gUVJorIzljILuWn3bEUpc3yjsFrRr/7RaZH28D67z2WnZjV3u9i20JxtH3dPTLc+6NhzU0umSfZDtYpGXlWn56+KKuVY9OzZky1btjhs79atG+vXr3d6TEVltVHTnIemICQkhJSUFPz9/fHw8EBVVWbOnMm0adMIDw8nOTmZBQsWoNfrWbRoEVOnTuXYsWO2t6sajcbhDX1FrFYrycnJfPLJJ3h5edmds6K6lDz0Vfd6pY0YMcKWwF1i4cKF/Pjjj6xcudLuQXnAgAEUFRWxZ88eIiMjSU9P5/jx47aZtGp6XHm6detGTk4OcXFxhIeHU1BQwLFjx+jatSutWrUiKSmJwsJCW3Bx1VVX8dhjjzFkyJBy26XkuIKCAlxdXVFVlbi4ONsDbFpaGnl5ebi7u5OamkpRUREBAQGYzeZKfw7Vfbu+adMmPvroI3bu3EnLli35448/eOyxx+z28fb2djjO39+fM2fOYLFY0Gq1nDx5ssrXrOn9jR07lvvvv59FixYRGBhoCxZatWplF5jm5+eTlJREaGgoBQUFDkPZSt/D6dOnbZ+d3cOKFSt47733qnxvQgghypddZOWejelkmVTAwl3r09l2dSDK2Ydm5cxpu/2tAeW//LOGd8AaEILmTHFCs2IxY1j+LYW3PVbuMQ1Bc2Q/mqQEp2Xa//bafVZLzwgFqCHhFDz6Crpta7G26YSldXusrcLB06fO62m5qA8F984md/OfuA8dg7Vjw08FX725rESt9O3bl9atWzNr1ixOnDjBvHnz+PHHHwkMDESj0XDXXXfx4YcfkphY/A/KYrHY/mFC8QJfGzZsICkpiU2bNtmNw3em5JyffPKJwzkrqktNr1fa8OHDbcOKoPjB8KWXXuKll17CaDSSkZFBRkYGJpOJ0NBQhg4dyiuvvMLJkyeZO3cu4eHhREVF1fi4yn4OnTp14sknn+T48eM8++yzuLi4MGbMGHr37k14eDhPPvkkCQkJfP311+zatcs2LKmkXTZv3szp06fZtWsXKSkp9OnThzZt2jB79mzi4+OZO3cuhYWFjB07FiieOWnOnDnEx8fz4osv0q1bN1q3bl2ln0N1ZWdnoygKOTk5bNu2jaeffrpKAWLfvn1xcXHhjTfeID4+njlz5lT5mjW9Pw8PD8aOHcusWbNsw6AAxo4dS15eHq+88grx8fHMnDmTdu3a2YZvlWfEiBEcO3aMr7/+mtjYWF599VW78q1bt6Ioii1xXwghRO38caLgbFBR7HCmmW3J53otqtNjgUaL6aopdpu0B3bWTUVrQXPaeVABoCuz4J2zwMnSoz+Fdz1J0eiJWDv1qJegooR5wEjir7gdS3XWwahDElg0IJ1Ox6JFi2xv1ZcsWcKiRYtsb6Q///xzfvnlFy6++GJeeeUV3n//fdzd3W3Hz5gxgxMnTtCjRw/uu+++Kk1f+/nnn7Ns2TKHc1ZWl5per4TBYOCZZ57h/vvvp6ioiJ07d5KYmMidd95JmzZtbH+WLFkCwAcffEBubi69e/fm77//ZsGCBWg0mhofVxGNRsPChQtJT0+nf//+rF+/nm+//Raj0YhOp+Pbb7+1Ja9//PHHLFy40C4RferUqXh4eNCrVy8mTZpEdnY2Wq2WRYsWceLECQYNGsT69etZunSpbXhQaGgoubm5DB48mL179/LZZ5+hKEqVfg7VNWrUKMaOHcvw4cN55JFHuPXWWzl9+nSlU6d6eHiwYMECfvrpJ4YPH16tmSRqc39XX301GRkZdrkQXl5eLF26lLVr1zJo0CBOnjzJt99+W+nPtlWrVnzyySe88cYbjB07losvvthWlpWVxYwZM+xm9hJCCFE7y+Icp/0evyKFF3Zm8m1MrsNQpopyLADMfQbbfVbSzkAN8ydrQ0lNRrfuVzRHDjisU1ERa/uL6rFWTZ+SkZFRs7EuDSAzMxMfn/qL6i4UJcNzGsOzzz5LaGgod911V6Ncv6E1Zlufj+Li4ti2bRsff/wxf/75Z7WOrW5bz549m9atW1f5d1G+f+zJ1JwNR9q6YUg7116+WaX9olPkmst5lFRVsv+eipv5XPCR+/5PDnkIZXlMm4CSd24CmNx3f0A1+lVwRB3LycL92bvRpBQP47L6B9v+Xpnct79H9a35qIO60Ji/21XKsRCipp577rlaLwwomq9JkyaRnJzMF198Ue/XmjVrlgR9QghRh9aeLCg/qABcrUWs9unKSEM6rqmJaFBQvYyVntfqF4S2VGChpCY3aGBhWPaNXSBR1aDC6h/U6EFFY5PAQtQrRVHQ6x3nVhYC4O+//26wa0lQIYQQdevXuPwKywu0BiZ2f4wQdw3zOucwon1wlWZCUv0C4cRR22clLQnadal5Ra1WqGQo7bmLqxhWfl/hLpb23dAe2e+4vUPDJ0s3NZJjIYQQQgghqsVsVVl5wj6/4vtRfnw+rAV3dLLPE2ztqSPQFdtq1JWx+gXZfS67yF516H/5Hx73XY7bM3ehKTODkzOaUutAlKdooPOF7aytZWIQ6bEQQgghhGgiCi0qJqvK36cLSSuwMiHcDW9D03sPvDuliMxSs0H5uWgY0coFnUbhmgh3Zvby4uNDOXjpNdzb1ZOE4xlVPrfqZz+cSElNctzJYgYV0JX/KKs5vA+XH4uH2mrjYnB75WEKHn0VS/eLyz8m7nCl9bNc5HyGQmurulkL7HwmgYUQQgghRBOw7mQBt/2VZvfAvvRYPj+M8bObfj4ms4icIpVefnq77Q1p/Sn7KeiHhxQHFSWC3LU806dmE2CovhX0WKgqhh8+x7DsG6y+gRTcMwtr555Oz2P4Y6ndZ8VqRb/6h4oDiyosyKf6t8QaGIImOdFuuzVUAoumFwKXUdMF2oQQoqbke0cI0dDyzFambUy3CyoA1iYWciD93CQonx7K4eIfk7lk2RlmbM1s6GraLD2WZ/d5aEuXOju3tWyPRcq5Hgvd9nUYln0DgCYtGbd3nkJJjKMsJe0M2p0bHbefKT8RW0lOxPDzVxXWTfX0Bp0O1cNxoVnVN8DJEReWJh1YuLq6kpeXV/mOQghRR1RVJSMjo1ZriQghRHV9fiiX0/nO12vYcLZ3wKqqvLw769wx/+aSkGNm3ckCfo/Pp8ha/y9FVFXl6e2Z/JthP+NjXQYWZRfR0yQlgKpCkQnDwvftypT8XFugUZpu8x8oTta/0GSmOr9ofh5uL95fad2s3sUra5cdrlVcmcbpPWpKmvRQKBcXF8xmM5mZjReRNwdZWVl4eztG1qLuSVs3nPpsay8vL3QVjNsVQoi6ZLKozDuQU275hlOF3NLBnYc3Z5BeaB889FiaREk80d1Xz7cjfQnzrL/vr8/+zXWoa0cfHW28tHV2DdXoh+rqhlJQPOuUUpCHkpmGcvoEGieL1WmPHipzAhX93384PbeSmw2mQjDYB0K63X+jyUyrvG7eRgCKRl2NbscG23bTpTdUeuyFoMn/n1PeGtZecnIyYWFhjV2NC4K0dcORthZCNBe/xOaTVE5vBcDm04XctzGd3+IdV7ku3UmxL62IO/9KZ9V4/3rJvSgwq7ywK8tum5tW4Z2Bxrq9nqJgDQpDWyqRWnMqHk2pKWjtdi/TC6EkxqFxMjzq3P5pDr0i2oO7qlS1kvU0LJ17YRo9Ef365VjbdKRo3PVVOr65a9JDoYQQQgghmrvP/s21+zy1swd+Luce0bKKVKdBhTPbz5jYn14/C9PuSjGRVSoHRAFWXObPwOC6GwZVwtrS/sWRcvoEmvhyAouCfMg/N3S+bFK1w/4ZjsOhlIyUKtVL9Q8+e4CC6ZYHyf10Ffmz3m/YlcGbMAkshBBCCCEaydFMM9uSTXbb7unqwYTwmi/queTouYfsrUmFDP0lmYE/J7E+sbCCoyq3tUw9J7Z1o5e/oVbnLI812D6w0JxOKDewAPteC6WSIU3OgoiKzm1XrzI9HcKeBBZCCCGEEI1kwWH73oqBQQY6+Oi5qb17jc/53v4c3t+fTXSKiWv/SGVvWhEH081M35SOpRYJ3luT7AOTwfXQU1FCLdNjYVj5vd3QqLJK90IoWY55GKVpMuwDDyUzrUr5FQBqQHCV9rtQNfkcCyGEEEKI5mhFfD7zyyRClwQU/QMNtPPWcjTLUqNzz/4ny2FbQq6FI1lmOhn11T6fVVUdeiyiguqntwLA2rJ1tfbXpKdiBVBVtP/usStTXd1RCs714ijp9j0WzlbbVrU6FIvjkDKrv/RYVER6LIQQQgghGtjKE/nc9Gca5lIdCN4GhSvbuAGgKAqv9jeiKZMTvXiUH2sm1Hy9hD2pRTU67miW2S6/wseg0MlYf++nraFtsfq0qPL+SmYqqCqu855Fd2CH/blat7f7bFj+LdrozWAubgslxX5ti6IBoyh49BWn11H9gpxuF8UksBBCCCGEaGBPb3fsUXhvYAu8DecezUaFuvLeICPuuuLo4vGeXowNc6WXnx5v/bmIQwFWjw/A21D5zEzRqaZK93F6XIp9QBLpb0BTn+s2aHWYB4yu8u5KRiqa+CN2U8CWsIS3d9jm9vZTuM+8tXgGqbRkuzLVNxBLx+7OLyRTkVdIAgshhBBCiAYUn2PmSJb9MJvX+vtwVVs3h31v6eDBgeuDOXJTMLN6F6/do9MoPBF5bh2fOzp7cHGggdhJLenpV/Ewp7IBQlVFl+np6FXJdeqCedDYcsusvmVW585IRbtnq/N9wzs43a5JOY3h5wUoaWfstqu+AWBwwdL+IvvzSG9FpSSwEEIIIcR542immV9j88kylb/uQ1OVZbLy+NYMeixJstse6a/n7q6e5R7XwkWDv6v9AnT3XeTJ1qsDWTMhgDeifADQKArfjPDlinBXdAq4auG+i+zXA9ubWlTlBG6rqrLpdCELY3L5oEwuSC+/+suvsF2/dTtMl9/ivKxdF7vPSnqKXR6F3b5tO5V7De2/exwCi5KgpXDS/ailemWKhl5WpXpfyKQ/RwghhBDnhXUnC7jxz1QKLRDqoeXvqwLxMZwf70izTFauWJni8OYfYExozaaW7ewkCTvMU8f/RvhhtqoUWYuDi++O5JNWWByI5ZhV7t6QTk8/PePCXDG6aAh0c1w1OynPwq1r09h+xvnQqV7+9d9jAWC6diraw/vQ/mefkG1pfxG6f9bbPmvSz0CSd9nDgeJEcFVvQClyvBdNZhpKYb7dNtWvOLCwtutC4W2PoV/7K9bw9hSNuaa2t9PsSWAhhBBCiCYv02Tlvk3pFJ6dJCkh18LCmDzuu+jcm35VVTFZwUVb9bH//ySbOJxZxPjWbhhdahekqOq5noAiKxjO1mN1QgHXrXZclK3E0JZ1P22rTqOgO3s7g4MN/Bp3boG9H47n88PxfJ7ZkYVGgZvbuzOnrze+Z3tFVFXl9r/KDyqC3DSEezoGI/XF0rqdY2DRsYfdZyU1GY22nMdarQ7VNwAl6aTTYqXAPrCwtjiXHG8ePgHz8Ak1qPWFSQILIYQQQjR5z+3I5FSe/fCnp7Znsj+tiBvaudHBR891q1P4N8PMje3deXegEV3ZKZXK+Ol4Hrf/VbzmwVve2Wy+KqhaQQkUByZ3rk8jPsd+WliDBmb09OKatu4VBhV6DfSpp0XmSgxt6WIXWJRmVeHrmDx+OJ7POwONDA524Z4NaWxOKj/J+8WLfVDqM3G7DLVMPgWAtVUbVA8vlNxsABRzEUpiXLnnsPoGoiknsLC7lt4AXj41r+wFTgILIYQQQjRJFqtKaqGV/WlFfPmf8/Hz3x7JY/HRPCyl0gYWxuTR2ajjgW5eDvtbVZUsk4pFhWd2nJuZ6WiWhZUnCmzTvVbEqqosiytg1YkCvj3ivF4mK7y8O5uXd2dXeK7uvnpcdfX7kD4spPIekTyzyr0b06ks/eLlfj5c167mi/fViN5J4OXiitU/GG1uxe1r7jsUcB6cOKP6BkADBk3NjQQWQgghhGhyEnMtjF9xhuPZlS8QZ3HyMPzCziyui3DH31XD63uyOZBWxCWtXHhnXw4JORa8dW5kmu3PvfFUYZUCi/kHc5m1PbPK91KRPgH1nwTd3ltHF6OOQxmOC76VVpWc7qudzFxV38xde1M6NLKcXZdC9QuCuJhyj7P6+GKacHPxvlXshSg9DEpUnwQWQgghhGhyntuZWaWgojwmK/wSm89/GWa++C8XgN/izw0HyjQ7vpXeUU5OQVkLzp6vLnT3rf8kaEVR+GhoC2b/k8WGU4U1Ps+4MFdaujdcbkUJtVUbzH2HotuxAVWrw3TlFACs/sHlHmPuM4SC+54BXfXa13JRn1rV9UIngYUQQgghmpTTeRZ+Op7vtGxOX2+MBg2/x+ezKqHih+Tnd2aRa67a1KoAe9OKyC6y4qUvP4k7pcBCTKbzN/86Ba5s48bv8QXkO+lGmdPXm6XH8tmXdnbFZ+oncduZnn4Gfh3nT6FFpd23p8g52y5uWoVbOrjz6b/Og6X3Bhk5kmnGoFW4t6uH030aQsH9c9DE/ofq6YMa0BIA1b/8dSUsbTvZBRXW0LaVXkN196DosptqX9kLmAQWQgghhGhUqqqy6Ege25JN9PQzcDLXTFGZZSpauCjM7OXNPV08UBSFKZ08mLElg8/KeSAGqhVUQPFQoDUJBVzd1nkOgcmicsufaQ7bPx3awi7vILXAwsxtmexJLaKFQcMjPTwZFeqKXqPQzVfPrWvTyDOrTL/IkzZeDfso5qJVeP5iHx7fmoFKcbBzXTt3Fh/NI6vIvr2WjPZjdA2nwq1zioK1bWe7TRX1WFiDQu0+m/sNR100HyXXccXzEqYrb5OVtWupSvOq/ffff4wbN47Q0FCuvvpqTpw4AcD+/fsZNmwYwcHBjBs3jtjYWNsxFZUJIYQQQpT4Na6A+zZl8NXhPB7dksGbe+0XY5vU3p1/b2jJvV097WYjmtXbm6EtXTAaFKZ19SB1SgihHrUbqvPxQeeBisWqcuu6NLYm2w+XmtbVwyGZ2c9Vy2fDfPlnYhB/TAjg0tZu6M/OUDWylSuHbwzmwPXBvNivcWYfuqOzB//dGMzhG4O5u6snLVw0PBlpvwaEAvQLrP/8j9qwtmpTbpka1Mp+g4sbec9+iGn8JArunIm5S6Tj+coeI6qtSoHFHXfcQf/+/dm+fTutW7fm/vvvx2q1MnnyZCIjI9m1axdBQUHcd999ABWWCSGEEEKUtvSY85mVStzdxcPpNLAtXDT8Os6f2JtDmNvfiFajMKpV7YYWbU028V+G4yJ2c6OzWXXCccrWqKDqX89Tr6FVLQOg2vJ31dqt5n13Fw+GBJ8LJK6NcGvyiw+qwWGYL+rrtMwa6BgkqEGhmK6/G/PQS22L4NkdI4FFrVXa35OXl8eBAwf48ssvCQkJ4YYbbuDaa69l8+bNHD9+nNWrV+Pn58eMGTMYMmQIcXFxnDhxotyy8PDwhrgvIYQQQpwndlaQNK0AnZysMF2eAcEuLDhccaBSmX1pRXbX3JdWxDt7Hac19XPRMLwKU7meD7QahSWj/fn2SB5aBW5o6Clla6jwtkfRPT7JbpuqaMCt4vqrno69RWpgSJ3W7UJUaSjq7u5OREQEf/31FwDr1q2je/fubN26lbZt2+Ln5wdAt27dcHFxYdu2bRWWCSGEEEKUSMy1kFhm4bvSIry1uFVjnYeoOhi+E1tmNqo39mRRNl1jdCsXfhzr1+Tf6leHq07hjs4eTOnkUe9ra9QVNTCEwpvsR8VUaWYni5ME/GrOICUcVSlD5YsvvmD8+PE8/fTTeHh4sHbtWj788ENb4ADFU5kZjUaSkpJISkoqt6w8MTHlz0Msak/at+FIWzccaeuGJe3dcC6ktl6bogXKf+vvjala7aGqANV7297Bw0pM7rkAYU9CGjHup4HihO51CW4U950Um9u5kFH+eZCWToxjLrcoR739XrftSZsufWhxaCcWgyvHew0jp5Jrefu2ol2pz/n+LZvVv7v6upcOHTpUWF5pYFFQUMBdd93F448/zpgxY/jkk0949NFHad++PapqH75brefeOFRUVpOKipqLiYmR9m0g0tYNR9q6YUl7N5wLra0XZmYCOeWWXxTkRYcOrat1zhtPp/HdUefT1QK46xTyznZBuOsUHo70ZfqmDFt5quJOhw7hZJqsjFt+hizzubfb3gaFe6LaopHVmaul3n+v/+91cs+cQvXwpKWTYU4OItpi2bgMbWIsqkYDtz7QbP7dNeZ3SKWBxdq1a8nNzeXhhx8G4LnnnqNNmzYMGjSI1NRU235Wq5XMzEyCg4MxmUzllgkhhBBClDhSZk2ITj46/iu1bVL76o/1fyLSm02nTSTkWri6jRtXtHHl9r/SAWjlrmXDlQG8tOkEpxUvpnb2oLWnfSJ1bHbx9adtTHdYrToq0CBBRVOk0TjOBFURrY785z5Cu/8f1KBWWEMj6q9uF5BKAwuNRoPFcm6sYcnfo6KieOmll0hJScHf3599+/ZhMpmIiooiNDSUF1980WmZEEIIIUSJ49n2D+5vDjSy6EgeW04XclN7dwYGVz85uo2Xjj3XBpFusuLvqkVVVXzHatibWsTlbdzwc9UyLbyIDh2Kh20XWlQUoGSsRWKelY2nCvk9vm5mgRJNlIsrlj5DGrsWzUqlGUd9+/YlLy+PDz74gJMnT/Lyyy/Tvn17BgwYQEREBC+88AKJiYm8+eabDB48mLCwMKKiosotE0IIIYSA4mHTZROlO/ro+GBwC3ZdG8zjvbzLObJyWo1im05VURSGhbjyQHcvpwvSuWgVh+lfb1yT6rAfwKCgpr22gxCNqdLAwt/fnwULFvDNN9/Qr18/9u/fz4IFC9BqtXz11VdER0cTGRlJcnIy8+bNA4r/AZdXJoQQQggB8Nm/uXarY3voFAJcG2eWpfY+9gGHs1W7L2vt2uQXjROiMVVpVqiRI0cycuRIh+3dunVj/fr1To+pqEwIIYQQF7YV8fk8vjXTblsbL63dytoN6doIN/5KLHRa1tWo47PhvnQx6hqtfkKcD5rP5MtCCCGEOG8s+C/XYVtbJ8OUGsqk9u5093W+jsFrA4x0baGXoEKISkhgIYQQQogGVWBWWZXg2DsQ7K51snfD0CgKV7d1c9juooX+MvxJiCqRwEIIIYQQDervJOdDjnr7N+7Kx738HK9/UQs9eo30VAhRFRJYCCGEEKJBLY9znMa1i1HH1W2rv2ZFXerpJLDo0qJxgx0hzicSWAghhBCiwRRaVH44nme3bVakFxuvDMRN17g9A36ujkOxwj0bb3iWEOcbCSyEEEII0WBWnigg03RuKlc/Fw0PdfdC10SGG93W8VyviU6B6yIatxdFiPNJ402/IIQQQogLzoZT9vkV10S4YdA2jaACYFZvb07nW/kvo4gHu3nR1lselYSoKvnXIoQQQogGczijyO7zsJYujVQT5wLctHw3yq+xqyHEeUmGQgkhhBCiwRzJMtt97uAj7ziFaC4ksBBCCCFEg8gusnIqz2r7rFWgTSMuiieEqFsSWAghhBCiQRzNtO+taOOlbVL5FUKI2pHAQgghhBANouwwqPY+skaEEM2JBBZCCCGEqBeqqtr+O/9ADlPXp9uVt5cZl4RoVuRftBBCCCHqlMmicu/GdNaeLODycDcuDjTw5PZMh/26+UqPhRDNiQQWQgghhKhTHx/M4cfj+QB8HZPH1zF5DvsEuGq4Ity1oasmhKhHElgIIYQQotasqsp3R/LYcKqQ747mV7r/oz288NDLiGwhmhMJLIQQQojzUGqBhRYuGjRK48+qlFFoZdrGdFacKKjS/nd08uDuLh71XCshREOTwEIIIYQ4j1isKlPWpfFbfAHhnlp+HONPu0ZcZG5/WhG3rk3leLal0n1dtbD28kC6tpDcCiGaIwkshBBCiPPE/rQipqxL5WhW8UN8XI6FqRvSWD0+AJ2mZj0X/ySb+D0+H4NWodCiMqSlCyNblZ/7UGBWOZ1vIcRdy4/H83lkcwb5FrXS69zcwZ17u3pKUCFEMyaBhRBCCHEeiM02c/nKM6QX2j/E704pYsF/uUzt4lnusYm5FtafKuTiAL3d2hEH0oqYsPIMhaU6G97dl8N3o/wYG+aK2aqi0ygczzKz8XQhW5JMLI/LJ6uo4kDCTauQb1EZGGRgdh9vevsbcJGF8IRo9iSwEEIIIc4DHx7IcQgqSryzL4c7Ons4zbc4nWdh+LJkkvOtaBX4c0IAvfwNAHx0MMcuqABQgRvWpNo++7poSCu0VqmOt3V055X+Rlx1CkVWFX0Ne1GEEOcnmY5BCCGEaOKS8iwsdDJla4mEXAvjlqeQZy4OAI5mmvnwQA7bkwt5aVcWyfnF2y1qcY/E8Swzr0dnOZ0GtqyqBBUuWvhgsJF3BrXAVVccTEhQIcSFR3oshBBCiCbsRI6Z29alkWuuePjR9jMmHtmcweze3oxefqbcgOCn2Hz+SCio9HxVpVHg4yG+XNXWrU7OJ4Q4f0lgIYQQQjRRL+7K4p292ZSNAbz0Co/28OKFXVlYS5UtPprPgXRzpb0MtQkqvPUKPfz0nM6z0tGo46FunvQPcqnx+YQQzYcEFkIIIUQTtPOMiTf2ZDtsH9rShaWj/TBoFTobddz0Z5pd+f60ojqvi14DfQMMTOvqyeBgA76u2jq/hhDi/CeBhRBCCNGAVFVlb1oR4Z46jC7lpzpuPFXosG1YSxf+N8IXw9kZli5t7cbaCQGMWn7GrueiOu7p4sHkjh7sOGMiKshARx8db+/LYVlcPpeEuPB0b+8msQifEKLpk8BCCCGEaCBWVWXiH6n8lViIqxZ+HedPv0DHYUSn8yw8tzPLblsXo44fx/ihLZMU3ftsT8IHB3IcztPdV8/S0X7E5ZgZszzFoXzr1YF0NhZPP3uR77lpaB/t4cWjPbxqdI9CiAtXpbNCxcXFYTQaHf7ExcWxf/9+hg0bRnBwMOPGjSM2NtZ2XEVlQgghxIXoj4QC/kos7okosMCMLZmoqn1XQ44Zhv6a7HDse4NaOAQVJWb19qJrC/t3hV56hQXDfQly19Iv0IXrI+yTq7v76m1BhRBC1IVKA4uwsDBiY2Ntf5YuXYper6dFixZMnjyZyMhIdu3aRVBQEPfddx8AVqu13DIhhBDiQvV7fIHd571pRWxNNtltW5uqtU0PW1o33/KDAHedhsWj/IjwKs598NYr/O8SX9r5nAs27u3qSek16u7vVv6CekIIUROVDoXSaDQYjUbb582bNzNw4ED27t3L8ePHWb16NX5+fsyYMYMhQ4YQFxfHiRMnyi0LDw+vz/sRQgghmiRVVfktrsBh+zcxeXTw0fHZoVw2J5nYcMpxaFRXow43XcV5DmGeOjZdFciOM0V09NER7G6fYN07wMA3I3z5KTafQUGOPRhCCFFb1c6xWLlyJTfffDNbt26lbdu2+Pn5AdCtWzdcXFzYtm0b8fHx5ZZJYCGEEOJC9H9bM51OA7swJo+V8QWkVjBF7OCWVZvO1V2nYWgF+17a2o1LW0tAIYSoH9VaefvEiRMcPHiQsWPHkpSUZAscABRFwWg0kpSUVGGZEEIIcaHZnWLi039zyy2vKKjo4auXRGohxHmhWj0Wq1atol27drRv3x7AIeHMaj33xVhRmTMxMTHVqYqoJmnfhiNt3XCkrRuWtHfN7MjQMG2/a42O7eJp4fOueWSfzMRxRQtRW/I73bCkvRtOfbV1hw4dKiyvdmAxZswYAIKDg0lNTbWVWa1WMjMzCQ4OxmQylVtW04qKmouJiZH2bSDS1g1H2rphSXtXn9mq8vLuLN7a7zgNbFXddpEvHTpIknV9kN/phiXt3XAas62rPBQqLy+PjRs3MnbsWAAGDhxIbGwsKSnF82Lv27cPk8lEVFRUhWVCCCFEc6eqKneuT+OtvY5BhasWRrdyngdROj37xnZuTO3sUU81FEKIulflwOKvv/5Cr9czcOBAAKKiooiIiOCFF14gMTGRN998k8GDBxMWFlZhmRBCCNHcrUoo4JdYxxmgAMaFudHdz3Hq2D7+evZdF8T/tTPx+6X+zB9S/roVQgjRFFU5sFi1ahXDhw/HYDAAxQnZX331FdHR0URGRpKcnMy8efMqLRNCCCGau48OOk/UjvTX83I/HyK8HUci39HZg1BPHde1NDMw2AVFkaBCCHF+qXKOxbvvvuuwrVu3bqxfv97p/hWVCSGEEM3V90fzbKtrl1h3eQA9/fRozgYLUYEGFKBkmpNBwQZuau/esBUVQog6Vu11LIQQQghh70SOmaXH8tmTWsTPsfl2Zf0CDET6G+y2tffR82I/H+YfyKGjj46PhrawBR1CCHG+ksBCCCGEqIUsk5UJK1KIy7E4lBk08FI/H6fHTb/Ik+kXyYxPQojmQwILIYQQooYOZxQx8OdkzKrz8rn9fbg40OC8UAghmplqrbwthBBCiGKpBRYuW5FSblDRzlvLlI4yXawQ4sIhgYUQQghRA+/syyGlwOq0zEOn8OHgFuhkulghxAVEhkIJIYQQ1XQm38KnhxwXv7utozuP9PAi0E2Lm06CCiHEhUV6LIQQ9Sa90MpvcfmcyDE3dlWEqFML/suloEyu9p2dPXipnw/hXjoJKoQQFyTpsRBC1ClVVXlye6bdAmFuWoUVl/nTy995EuvWpEJWxBcwKNiF0aGyMJho2kwWlc//tV8A78WLvbm/m1cj1UgIIZoG6bEQQtSppcfyHVYdzreoDF92hjk7MrFY7TNd96aauGJlCu/uz+H6NanctykDq1pONqyosvpoQ4tV5Y8TBfx8PJ/88jKWG5DZqqI24O9KRqGV16KzCPxfIqfzz+VWeOgUbukgSdpCCCE9FkKIOqOqKu/vdxx3XuLtfTm46RT6BBj4JTafoS1d+PNkIaZS+a+LjuQR6afn7q4yv39VFZhV3t6Xzf60Ik7kWNibVoRBA7N6e/NQ97p5i55RBJevTGFzkgmAMaEuLB7l1+C9SzGZRRg0CouP5vHevhwMWoUPBhu5tLVbvV7XZFG59PczHMpwHNY3qYM7Rhd5TyeEEBJYCCHqzI4zRexNK6pwn5d3Z9v+/r/DeU73+b9tmVwc6LhacXORVmDhgb8z2J5swk2nMKqVKw919yTcy/4rOTnfwgOb0onNtjA0xIWHunkS6un4tf3sjkw+PmTfS2SywpydWVzZxo02XrX7qldVldn/ubA1w2Tb9kdCIb/HFzA+vH4f6EukFViYtjGdVQmF9gVmlSnr0vjtUn/6BbrU2/V/is13GlS4auHRHjIESgghQIZCCSHqSG6RlStWptTZ+S5ZdoboFFPlO55nVFXlrg3pLI8v4EyBlfgcC1/8l0vPpUn8GptPbpGVPakmZmzJoON3p1mVUMh/mWY+PZTLmOVnyDQ5Tm+65mSB02tZVVh1wnlZdaw/VcjWDK3D9pd2Z1V7KJJVVfk1Np+716dx74Y0jmdVnti/P62IYcvOOAYVZ5msMPufrGrVozr2pJq4Z0O607KHu3vR0t2xbYQQ4kIkPRZCiCqxWFVe3p3FNzF5hHpouaerJ9dFuKEoCn8lFnDjmlSHWXJq66ODOXw01LduT1rHfonNZ3eKCb1GQQWubONGd199ufv/cDyfP086f0CevC6twmsl5ln54EAOT0V627ZZrCrxOeU3/OqEAu6p5bCy1/dkO91+MN1MiwWJLB7lx9gw10rPk1JgYer6dP5KPHf/B9LNbLgioNwhVRmFVq5bncKpPOfrRZTYnmwi36zW+WxMx7PMXPa7Y8B8TVs3Lm3tysS2DdNjI4QQ5wMJLIQQVfLMjiw+OFCcP5GUb+XuDenEZJq5uYM7N/+Z5hBUdPTRse3qQBRFId+sMviXJI5mVS/y+PNkIVZVRVNP4/gPZxTxzxkTw1q6OB1iVMJsVXl+ZxY7zpjoE2DgohZ6RrZyYcmxfJ7anmm37xt7stEoMCjIwO2dPBgV6opBo+B69oF3/oHyc1Cq4sP9OUxs60ZnY3HwcjLPQlEFz9ybTheSZ7birqt+B/Wh9CIe/Dudf85UPLztzr/S2Hd9MC0qyDP4N6OIa1alcjLP/ndgX1oR25JNRAUVD2Oyqior4gvYmmxib2oR6085D8LKUinuWUjOtxKfY8ZDp0GngWB3LSNbuVT7d8iqqlhVuP/vdHLLJKpf1tqVz4c37YBXCCEagwQWQggH25ML+e5IPl1b6LijswerThTYgorSXt+Tzc+x+Q4PXgD/18vL9hbaTafw7qAWTFhR/lCppMkhxOeYufjHZNu2MwVW9qYWlTtNbW38k2zishVnKLKCv6uGNRMCys1FeHl3Fu+dTUovSV6uiFWFjadNbDxdvK9WgSEtXdiTaiK9sHazGOWYVW7+M5XNVwXholU4XiZYi/TXk5xntT3AF1iKc1+Gtqxe/sG2pEKu/iOVvCrM/pRjVvn6cC4PlpMorqoqU9enOwQVJb47kkdUkAsmi8pNf6aW26MDcFUbN64Id8XHRcPHB3P4o9TwqHFOehYAHuvhyew+PpXeR4kPDuTw4s4s8i2O997bX89bA4xVPpcQQlxIJMdCiCYgvdDKJwdz+O5IntMx9HXhm5hcBv+SzJ1/pXEwvYjHtmTQ54fTXL0qhRXx+bb99qSauHxlCl/8l8uMrZm8ty+Hx7ZklHvemEz7MfK+Lhq+usSXayPc7bYPDnbh9k7220rc3cUDF61CBx89l4fbD6lZU8FDZm28sCvL9qY/pcDKS7vsx+jnma1M35ROxLeneGtv7XoZLCr8lVjoEFT0DdAzrWv1pyk9mmVhTUJx7kRstn37t/PWMSzEPojYmuS8DS1Wld/i8vn6cC4Zhfa/d49sznAaVDzXx5sBQY6B3scHc8lx0nWyO8VE2Den2F9BUv8vcfmoqsrMbRkVBhWhHlo+GtKCiRHujGzlWuGQs9LmHcghpYrj9GKzzcz+J9NpUDGspQt/TgggWHIqhBDCKemxEKKRFVlVLl+ZYnvwauGisHCEHwOD62aGmwKzynM7zy1Ytz+tiB+OnwskjmZZWJdYyFsDjBRYVIehPc/trHpSrE6BXdcElTv15py+PhzONPP36XNv/bsYdTxZKmdgZCtXlsWdSzjeklQI1HzWnc//zeH3+AK0Clwb4c717dyJTjGxocwQmyXH8mnrncUdnTwIdtfy8cFcFsY4n7Wqrlwe7sZ9F3niplMcgpfxrV15urc3SfkW+gYYuHtDOr/Hn2uXX+PyGR/uxvEygUUbLx3hnlq+PXKu7lvK6WV5Y282c8/O0vXtkTx+G+ePVqNwLMvMQSczIJXU+ab27jy6JYPlpepzMs/CwJ+T+WmMP+18iv/Xcii9iCtXppBTJkAZEGTgn2QTJZvTC1V+jSvgy/8qbu+r27rZhpQBdGlRtcCi0AJf/ZfHYz0r/j0yW1Ue3ZyB1UknTaiHlk+GtpDFG4UQogISWAjRyDacKrR7m5teqDJ3dxbLLg2o1Xnjc8ycyLHw8OYMh14FZx6toFeitOkXeWI0KLy02zGhd2hLlwrn8/c2aPhtnD8pBVZ8XTSkFVrxd9XYPawNLPM2fGuSiYf/TmdVQgGjWrny9kAjOk3VHu6+O5LHY1vOBUp/JBRydzmz+wC8Fp3N69HZPNjNk83lvOV35roIN+b292He/hze2Ve13g1vvcK1Ee7oNQrP9PHhgW5eTNuYzpFMM7d0cOeBbp5oNYrt4XlaV0+7wGLFiQJMFtUhsGjrpaV/oH0b/pNswmxVHdptwX/npqjdkmTiz5OFjAlzZXWC85mkbu3gbgsaFo70Y9b2TLshcvE5Fib9mcqWqwPRKApPbMskq8jxKX1KRw8Scy3ElUo6n1JJ4jrA6FD73qwuxqoFFlC8Pkp5gcW3Mbm8vDubhFznvRp9/PV8MtSXIOmpEEKICklgIUQjWxab77DtYHrlgUB5rKrK3RvSWXrM8bx14a4uHrTy0PJvhtmu5wPggW6Vzz6kKAoBbsUPaCX/La2Dj84WdADkmlUWnF3v4uuYPPoFGri1Y+XDh1RV5e29zmczqvA44N0KFvkr66IWOuYNboGLVuHZPt60dNdyML2Ika1cCXbXoKpw78Z0jmefe2gdE+rCoz28aOVx7v5buGj4bpRfudcZGGTA31VDSkFxu2SZVGZszeCXWPsgoK2XjnbeOrt9c8wqR7LMtoRvgNQCi8NMS4uO5DE61IWfy/xO3tLBnZHuaVwVGWK3/clIL5bF5dvNSvVfppnYbAsh7lqnidd6DYwLc+Xzf3PsAovSFOC7UX5M+jOVkhFJ3nqFqDIBU0ejjjZeWmKz7c/TwkVh29VBdFl82nb8kSwzaQUWfF3tf+fWnixg+qYMysskWTLaj1GtXKSnQgghqkACCyEaUaFFtRtOUiK10FrjmXxWxBfUW1AxIMhgS3D+fLgvbw+0cii9iAPpZiL99XWyoJ2iKEQFGezezpf2c2x+lQKLX+MK+K8KPTVV5WNQyDTZP366aOGDs0EFFNfd2dSu2ycG8VdiIWfyLVzW2q1GqzRrNQrXtHWzWwiv7AKDGgUu8tWjKAqdjDpSSg05S8y10NmoZ2+qiSnr0uwCnRIrTuQz74DeYejU/d080Z5JcXi49tRr+HWcP72WJtltj04xYfFz3pvwZKQ3RhcNgW5awHnexW2d3Bkb5sqs3t48vzMLjQIvXOyDQWt/fb1G4dOhvszdncXas1PYas/uG+impUsLvV1v4OUrU7guwp07u3jgpdcwb382T1ew/sWAIINDL4kQQojySWAhRCN6c282ZwqcJ2ufzLXQwaf6D6BlV2Au4WNQeG9QCzadLiTbZKVvgIH5B3OcTgH7RpQP7++3f6OsAE/0sh9K4m3Q0D/Ihf5Bdbvi8bCWLuUGFv+cMfFfRhFfJ+gIKMjmstaudPCxf4jdl1bE9I3lD3kqEeym4a8rAnlsSwZ/JRY6nd0KoLuvnnWXB2C2wrFsM3tTi9iTauLaCPcqzVil1yh18oD6UHcvvjqcW+56Ib399fgYin9nQsoM25m7O4ve/gZuKyeogOIZpMouNDcgyEAnHx1Hzji/ZhsvHQ9287TNmgUQnVqEt8H+d7e1p5Zfx/nbAtMgJ71VULyS9eM9i3NuHu3hxY3t3NFpOBuIOLo40MCPY/0BOJppxlWn2HqCLg6wDywOpJs5sDOL/zLN9A3QVxhUALwRZaywXAghhD0JLIRoJJkmK+/tK3+oTkKOxeGBuTL/ZRQ5JCUDhLhrWDjSj0h/A1e2Obeg11+JhQ6BhV4DkzoUJzmvPVmIohQPr7qohZ6O1RjTXhu3dfLgq8O5ToeEZZlU+v+UDBggNou5u7PYclUQbb2Lv87SCizcsNoxYdiZmb28CXbXsnBk8RCk74/mcc+GdIdhMXd29kCnUdBpoGsLPV1b6LmxvfMZrupTiIeWy1q78eNx5z1SpaeULTtz0T9nimjz7alqXU+vgbcHGisdBtSzTO9EdGoR4V721x8U7GI3nW+Am/Og+bUoIyGlhoiV/ntlSvI/SvQJMDhNCF90JI9FR8o/T1SggZXja5fjJIQQFyKZblaIRhKdUlThStUnykkkrciiI/YPUVGBBuJubsnOa4KdDlPqZHR8t9A3wIC7ToO3QcNVbd24so0bV7d1b7CgAsBFq7DisgBuaOdGZXnaBRb47N9zvTSf/ZtLYpncgVf6+5B2Wwj7rgvihnZuhHpoeaCbJ1PKTH97fTt3lo7xY1DwubZq46Xluoims7py2Yf40oYElx9Y1MR1Ee52eRnl6eVn/7sVnWriaJZ9UBjuaV8fZz0Wo1q5MLkKw9yq6uKAqg/N6+2vp723jgFBBj4a2qLO6iCEEBcS6bEQopEcyqh4NeMT5SS2lsdiVfn+qH1gMaWTh21ojDOdnDw03tap7h7sasPHoOHjob7M6Wth6K/JJOeXv77HtuRzvTRry6yDMLWzB/eezXsI89Tx8dCKV0we2cqVka1cic02E5dtpl+gC266ppO4262ctRu6+eoZUqrHIsS99u+N7u5Std+Ftt5auxyULJPKhwfsh+SFl1l8MNBJj0Vd5OiU1tFHx8UB+kpXDr+9kztvD5RgQgghakt6LIRoJIfS7R922pQZOvL6nmwG/pTEkcwijmQWVbpw3sbThXZv6t11isNic2WNauWCR6mH5skd3bm+Cb2dh+I3749Xsv7AjjNF/BKbz7akQrYl2yceP9y98pmqnGnjpWNYiGuTCioAujlZu8HPpXga39LTyVanx+L6dm546e3vc2JbtyqveK5RFCa2rfj3pmyPhbPAooNP3b7rUhSFz4b5Or1WaZKgLYQQdUMCCyFqqdCiYna2olYFMk1Wviozo4+zh5uDGWb6/phM3x+T6fTdKdafnfkmrcDC/w7nsuFUIapafO1vywyDujzcFU99xf/EfV21rBwfwD1dPHhnoJF3qjCevjFcU8lDKxSvgzD29xS7/Ih23lpCPZtXx6yztRRm9fZ2mGmqZQWBRYi7hmf7eHPfRR78PNaPj4e04KHu54K3/oEG3h9krFa97u5ScQDn2GPhfKrhuhbupWPjFYFc367836FBdbQYpRBCXOia1/9xhWhgX/6by6x/MnHRwr1dPemBQodKjrFYVa5eleKwfVyYK5+WM6MTFOcSvLgrE53Gh1vXptnWeXhvkJFWHlq+P2qf0DupisnF3X31vNrEZ7/xddUyuaO7w/SqlSmdzNycTGx7LoFbr4FLWzsGpcHlzKIE0MvfwCM97HuBHuvhSf9AA+mFVi5t7Yq+iosQlujSQs+VbVwd1tWA4hmhWpYZmuWsF6Gdd/38LynIXcsnQ31p45XFa9H2EyYMa+lS4XBBIYQQVSeBhRA1dCLHzIytGVhUyDPD3N3ZgBtztNl2b3/LWpdYyK4U+2FQ3nqF4S1dCPXQlrv6LxTP7HPTmlS71Ywf/DuDMmt+0cpdy+Bm9hb28Z5erDxRQHK+lRYuCveHFfDCkYrv8YZ2DT9zU0N4po83SfkWTuRYmNHTy2nvhGsFQ7g6OHmAVxTFLkejJt4f1AKLNZ3fSk0V3N5bx5sDjGjK9IS56zREBRrYenbo2sAgg8MUtXXtqUhvpnb2YH1iIW/sycbfTcMbA3zq9ZpCCHEhqdK3eFFREQ8//DChoaH069ePbdu2AbB//36GDRtGcHAw48aNIzY21nZMRWVCNAdf/JtrW9W3tOd3ZhGdYnIsOOu7o45v3YeFuKDVKFWafah0UFGi7OxSj/b0RFvNN85NXZinju1XB/HjGD+irw3mimAL8wYbcfYsqlHgpX4+RNXx+hpNRRsvHcsvDWDvdcEVzqLUN8B5onfbeuoZ8DZo+GakH1uuCuSvywNIvy2EHdcEMSzE+c/hi+G+3N7JnTs6efDpsIqT6utKoJuW69q5s21iEMsvDaj2lM5CCCHKV6XA4v333+fYsWOsX7+ea665hrvuugur1crkyZOJjIxk165dBAUFcd999wFUWCZEc2CyqA45EiUsKoz87QyPbE7n/f3ZFJRaTyE+x8xvcfZDlrz1Ci9eXPzW9OYO7pVOr1qZx3t6cUcTmdmprhldNIxo5WobunJLBw+Sp7Ri/RUBvHixN58Pa8Fr/X3YdnUg0y+qWdJ2c/JgNy+ngVfZxfPqWpcWenr5GyrN1wnx0PL2wBa8NdBoW9ROCCHE+atKr62+/vprvvjiC9q1a8f06dPp3Lkzmzdv5vjx46xevRo/Pz9mzJjBkCFDiIuL48SJE+WWhYeH1/c9CVGpArPKb/H5aIAr2rhxKMPMizszQVF4ro83XZzMvFPahlOFthwHZywqtoW5DqabmT+keCrLxzZn2PUutPbUEn1tkG2YSHsfPa/08+Hl3VlkmKqXEA4wJNjAU5FeTTIBuz719DPQ069upyptDq5o48bugGBuWJNqW4Ha10XD4JbSVkIIIepepYFFUlISx48f559//uHKK68kJCSEjz76iDVr1tC2bVv8/IpXrO3WrRsuLi5s27aN+Pj4cssksBCNLT7HzI1rUm2rOo8/ns/+tCLizq4bkVZgYfWEwArP8Wuc85WPnVl0JI8RIS7oNQqry6yx8FgPL4ex53d39eTOzh7E51jo91MSRRXPMmtnUgePCy6oEBVr5aHlxzF+PLsjizP5Fh7t4YW7TpKVhRBC1L1K/+9y+vRpNBoNO3bsYOPGjfTo0YO77rqLU6dO2QIHKE78MxqNJCUlkZSUVG6ZEHVp7ckCLlmWzMCfk/jzpP1sNLtTTIxdfobWCxMZu/wM25IKsaoqk9em2YIKgOXxBbagAooTpNMr6I3INFlZViawGBxs4KV+PkR6O0+8vmtDOrf9lWa3rX+ggVs7Ok8u1moU2nrreHeg0WF9i4qMatU8cwpE7QS6aZk/pAVLx/gzsJkl9QshhGg6Ku2xyMvLw2q18thjjxEeHs5dd93F6NGjCQ8Pt82fX8JqPfcwVlGZMzExMdWpt6im5ti+K5K1PHvYgErxG/rrV6fwYJsirgwy46aFKTtdiS8ojp23JZu4a20yj7UzEZ1a+WJYq/cdJ9LH+e/sK0f0pBeeGyrlpVV5LSIDvSaD4d3g/Vg93yVWnhB6RYtsjh7JqHCfi4ElPSHXDK8fM7AiWYtBAxOCzHT1tPJ8zLmHxK6eFjISjlHxGZuX5vh73ZRJezccaeuGIe3csKS9G059tXWHDhVPql9pYOHt7Q2A0WgEwNe3eOaOAQMG8M0339j2s1qtZGZmEhwcjMlkIjU11WlZTSsqai4mJqZZtW92kZWPDuTw0mH7+egtqsLbxw28fdz5+PH4Ag3zEjwBs9Nyu2t4BtGhgyfJ+Ra++DcXV63CXV08iM228MOmZLt97+3mTddOoUBxW380tg2vFFoZ+HOS3UrYpXnrFe7o17Zaqzov7FLcW+KhU9BpFCxWle0Faaw8UYBeA3MHBdIh5MJZQbi5/V43ddLeDUfaumFIOzcsae+G05htXelQqIiICAwGA8eOHQMgObn4oWrgwIHExsaSklK80Ne+ffswmUxERUVVWCZEbT22JYOXdmdXvqMTMZmVBxXF18jkSGYRt/yZxivR2Ty3M4tW35xi0C/2QUWoh5bHejiuWWF00fDrOH+uCHf+oD8+3K1aQUUJH4MG3dlpo7QahYUjfNl4ZSD/3hDMsAsoqBBCCCFE01NpYOHm5sb48eN54403iI2N5eOPP6Zr165ERUURERHBCy+8QGJiIm+++SaDBw8mLCyswjIhaiIpz8Lc3VncvT7NYYXpmuroo2NWpBfacp7v+/6YzPYz5a9HAXBdhFu5C5G199HzvxF+zIp0DDzKCziqS6tR6O6rx6/sCnlCCCGEEA2sSlODvPHGG2g0GgYMGEBsbCyffPIJiqLw1VdfER0dTWRkJMnJycybNw+gwjIhqiPfrLLjjIkhvybzanQ23x9zDCp0Cvw6zp+HunkSWo258IeFuPB4L28OXh/MmgkBNarf6NDKA4SRrez3cdXCJdK7IIQQQohmpkrrWPj5+bFkyRKH7d26dWP9+vVOj6moTIjKqKrKvAM5vL4nm6xK1nN4NcqHoS1dGNrShTkX+2CyqLy1N5sPDuSQ7WSV6hJDWxYnPge5awlw09DeW8eRrKoNlQLwMSj0C6x8PYBe/noGBBnYklTc+3F/N69yezmEEEIIIc5XVQoshGhob+zJrlIexforAhwWRjNoFZ6I9Oah7l6oqPT5IYlTZZKoFWBwqWk3NYrCh0OMXL0qlVxz1RammxXpbct3qIhGUVg00o8fj+fj56phfGvprRBCCCFE8yOBhWhy0gutvLU3p9L9Lg1zrXC15eLkaIU3Bxi5d2O6Xc/HHZ09aOFiPxKwX6ALJ28NYfLaVH6NO7cmRrCbhundPGnnraOdt44iK7hpFdr5VP2fj9FFwx2dPaq8vxBCCCHE+UYCC9Hk/O9wLvkW570GL1zsTWtPHRaryvhwtyqd77LWbsROcsWiwrqThajAiAoWkvt0mC9XxeWjURT6BOhp5aF1WB1bCCGEEELYk8BCNClmq8qnh3KdlukUuKGdO4Fu1Z8BSaMoaBQYE1b5MCQXrcLECOcrYgshhBBCCOcksBBNyvL4AhJyLQ7bDRp4tq9PjYIKIYQQQghR/ySwEE3Kgv/seyumdHTn9Sgj+RYVH0OVZkcWQgghhBCNQAIL0aTsSS2y+3xnZw8MWgVDeavYCSGEEEKIJkFeAYsmw2JVSS+0nxa2Swt9I9VGCCGEEEJUhwQWosnIMFkpPReUj0FBX4V1IoQQQgghROOTwEI0GakF9r0Vfi7y6ymEEEIIcb6QJzfRZKSWGQbl5yq/nkIIIYQQ5wt5chNNRtkeC19XmVpWCCGEEOJ8IbNCiSqxWFU+OpTLiRwzN3fwoLtv3SdVp5XtsZChUEIIIYQQ5w0JLESVPP1PJvMPFq8x8fm/ubx4sQ93dfEgz6ziqa+bAMAhx0KGQgkhhBBCnDfkyU1U6niWmU8PnVu4rsgKM7dl4rsgkfCFp3j2n8w6uY4kbwshhBBCnL/kyU3YUVWVX2LzeW9fNidyzOSbVZ7YloFZdb6/RYV39+ewPrGw1tdOKbDYffaVHgshhBBCiPOGDIUSNqqq8uiWDL78Lw+AZ3ZkVfnYGVsz+GN8AC1q0csgORZCCCGEEOcveXITNouP5tuCioponaxZF5Np5tLfz5BxNjgoMKu8vy+bp7dnEp9jrtL1JcdCCCGEEOL8JU9uzcSOMya+icnlTL6l8p3L8eV/uZXuM7mjO6/098HZetj/Zpi5/a80VFXl+V2ZzN6RxbwDOQz7NZmjmZUHF8kSWAghhBBCnLdkKFQzsPhoHtM2pmNVIcRdw6rxAYR5Ov/RWqwq8w7k8G+Gmds6utM/yAUAs1Vlb2pRpde6PNyN0aGu9PE3sD+9iM8O5bI37dxx6xIL+fzfXD48cC5ISS9UGbU8mcWj/OgX6OL0vKfyLJzIORcUaRVo6S7rWAghhBBCnC/klfB5LjHXwuNbMrCeTa5OzLPy2JYMVNV5tvWre7J5dkcWi47kcd3qVFvC9OFMM/mWcjK0zxoYZGBESHFg0DvAwOSOHvwxPoDe/vZrWszY6jhLVHqhysRVqexLcx68bDxln/zd219fZ9PYCiGEEEKI+idPbuepQovKi7uy6Pr9abKK7AOCPxIKeX1PtsMx2UVWXos+tz2rSGVNQiH/ZhTxarRjonaohxadApeGufLLWD9+HeePVmM/CMpVp/DixT5VqnOOWeXWtakUOglgNpQJLIa2dN6zIYQQQgghmiYZCnWe+uzfXN5wEjyUeHl3NqNaudI7wGDbtijGMTH7xV1ZnMy1UPZRf0ZPL56K9CLfrOJRSc/BgCADPf307KnCUKrYbAs/x+ZzQzt32zaLVeXPkwV2+0lgIYQQQghxfpEei/PUb3H5le7z4i77Xojf4gsc9klwElQA9PTTo1GUSoMKAEVRmDe4BUaDs5RuRx8dzLH7vOFUIafyziVue+iUcnMxhBBCCCFE0ySBxXnIqqpsSTI5bG/vbd8BtTaxkKG/JPPzaS25RVZ2nXE8xhl3ncKQ4Oo92Hf31bP+ikAmtHZ1KHtrgNHu8+6UIrtZohYdte9JuTzcFTdd1YIUIYQQQgjRNEhgcR7IN6vcvT6NPj+c5t192RzNcpy6Va+Bb0b6MjDIYLd9b1oRLx1xIWzhKXLKWz67jHu7emCsweJ04V46vhnpx+SO54Y5tXLXclN7dwYH29drS3JxToVVVfnjhH1Pyk3tPap9bSGEEEII0bgkx6KRWVWVDw7ksPm0ievbuXF1W3eHfV6NzuL7Y8VDn57dkUWMkzUhvhnhR2ejnnFhrmx20pthrVpMQZinlge6eVXvJsp4uZ8Pvi4aTuVZmNbVEzedwuBgFzadPlevrUkmbungwX8ZZjJM5yrnbVAcghAhhBBCCNH0Vem19KhRozAajXZ/zGYz+/fvZ9iwYQQHBzNu3DhiY2Ntx1RUJs5Zciyf2f9kseJEAXeuT2d3SvHDd6FF5XBGEYUWlXf22eckfFMmCfuR7p6MDSsegtTTr3YP5SsvC6BFDXorSvPUa3iurw8fD/Wll39xfQaU6Un5JiaPHWdMLCxzL1GBBoeZp4QQQgghRNNXpSfIzMxMfvzxR2JjY21/NBoNkydPJjIykl27dhEUFMR9990HgNVqLbdMFM+CNPufTEb9lsw9G9Jt260qvBadTWy2mW7fn6bfT8kE/S+x0vOVDiZ6+ukr2POcq9q4OWyb09ebVh71syhdnwAD2jLxwqjfzjDvgH3QFBUkSdtCCCGEEOejKg2FysrKIjw8HKPRaNu2adMmjh8/zurVq/Hz82PGjBkMGTKEuLg4Tpw4UW5ZeHh4fd3LeeODAzm8vz/HadmKEwW46xTOFFidljvTq9QCdVXJjWjjpeWToS3Ym2riWPa51a57VTEoqQlPvYaoIAN/n644gTwqUIZBCSGEEEKcj6rcY/Haa68REhLCxRdfzNatW9m6dStt27bFz88PgG7duuHi4sK2bdsqLLvQFVlV5h90HlSU+OF45VPJljAaFMI97XsZbu7gmKdR2hO9vDFoFV6NMtp6EXr76xlSz2tHvNLfWGF5sJuGPgESWAghhBBCnI8qDSxMJhMFBQUEBQWxefNmIiMjueuuu0hKSrIFDlC8loHRaCQpKanCsgvdp4dy7dZsqK2efgYUxX6M0cxeXrT10qJRioOMbyPzCXYr/lFfH+HGdRHFw6BGh7qy/eogloz2Y9X4ADRK/eY2dPfVs3NikNOyAUEGFo/2w6XseCkhhBBCCHFeqHQolF6vZ8+ePbRu3RpFUbjnnnv4/vvvOX78OKpqP9WQ1XrugbmiMmdiYmKqU+/zUkyuwuzdrkDdPTyHa3KIiUl32L64B+RYwEtXnBz9c+8css1g1Odx7Giq3b5tgNijdValSo0NMLDqTPGvnrdO5X+9CmjlmgdpGcSkNVw96suF8LvcVEhbNyxp74Yjbd0wpJ0blrR3w6mvtu7QoUOF5ZUGFoqi2OVF+Pj4ANC3b18WL15s2261WsnMzCQ4OBiTyURqaqrTsppW9HxisarM3Z3NyoQCXDRwY3t37uriyadbM7CQW6fXGt0piA7hjonYpcXExNC5Y9Np38/bWHl3Xw7phVbu7epBe5/6y+1oaDExMc3qd7kpk7ZuWNLeDUfaumFIOzcsae+G05htXelQqFWrVtGrVy/b54SEBACGDh1KbGwsKSkpAOzbtw+TyURUVBQDBw4st+xC8NqebN7Ym83+tCJ2phTx+NZMNp0uZH9akd1+k9q7E1qNWZjGhrkyuaM7+rM/tYFBBi4Nc1zpuqnz1GuY1dubNwYYm1VQIYQQQghxIas0sOjTpw+pqal88cUXxMXF8dZbbzF27FgGDBhAREQEL7zwAomJibz55psMHjyYsLAwoqKiyi1r7mIyi3hrb7bD9l9j8zmUYR9Y/F8vL7ZPDGT71YEcuiEYo6HiIVJTOrrz3qAWHLmpJduvDmT5pf7oZM0HIYQQQgjRBFQaWPj7+/PVV1/x8ccfM2TIELy9vfnggw9QFIWvvvqK6OhoIiMjSU5OZt68eQAVljV3i4/mU+QkneT3+ALSC8/lnXjoFFp7anHXaeho1NPSXcvrUUZbuZdeYd3lAbTzLu7RmNDalXFneyd8DMXHlE3aFkIIIYQQorFUaR2LESNGOJ0qtlu3bqxfv97pMRWVNRSzVSUu20KYpxZDA802dLhMr0SJhFyL3efORp3DLEzXtXMn0E3LzhQT41u70smoZ8fEINILrfi61s/CdUIIIYQQQtSFKgUW56M8s5UJK1LYlVJEsJuGD4e0YESr+s9HOJ5tqXwnoEsL57kFw0JcGBZybj0JRVEkqBBCCCGEEE1elRbIOx/9FlfArpTi3oPT+VZuWJPKoXTnvQl1RVVVYrPNVdq3vMBCCCGEEEKI81Gz7bHYV2YGpiIrDPg5mR6+xStMP9/XG20dJz6nFVrJLlIr3xEYF3r+zeYkhBBCCCFEeZptYBGT6bznYG9aEXvTiuhs1HFrR486vWZVh0GNCXWhnU+zbXohhBBCCHEBOu+HQu1NNfHirixWnsgH4MVdWXT7/jQrTxRUeNyakxWX10TZYMbf1Xnz3tDOvc6vLYQQQgghRGM6r1+b7001Me73FPLMxcOP7uniwceHqray9aH0quVCVFWBWeWjgzl224aHuLD0WL7Dvt19Jb9CCCGEEEI0L+dtj4XZqvLA3xm2oAKoclABcDTLTIG5avkQlckyWZmw8gx7Uu3zOspbFTvC+7yO54QQQgghhHBw3gYW3x7Jc3iQrw6LCjFZddNr8XVMHjvO2Nell5+eK9q4Od1fVssWQgghhBDNzXkZWBRaVF6Lzq7y/h46hRGl1oYosTvFVKt6LDqSx9T1aczanmm33aCB70b5oXcSQLjIkhRCCCGEEKIZOi8DixXxBQ4rWZfHVQvLL/Xnx7H+PNzd065s1vZM1pVJ4raqKk9sy6D7ktM8/Hc6Zqvz4VLL4vKZtjHdaQ7FZ8N8CXYvjiAmtrXvtbi9U93ORCWEEEIIIURTcF4GFn8lVm1Gp+6+elZcFkAvfwMAw1ra91pkF6lc/UcqG04V2rYtiyvgo4O5nMixsOBwHm/scewZUVWVN51sL9EnwGD7+30XeaI923HhqoX7L/Is5yghhBBCCCHOX+dVYKGqKofSi1idUFjpvle2cWXjlYFE+p97yB8e4sLl4Y4J1T8cy7P9vWwPxivR2aQV2PeO/HPGRHQ5+R0eOoUQ93PN2jfAwJ8TAnitvw9brgoi1FMSt4UQQgghRPNz3gQWFqvKZStSGPBzMifzzj3o6xSY28/Hbl+NAnd1cewZUBSFj4e2wNfF/ra/OpzHsbOJ3DtTHAOGT8rMNuVs+FOJME8timKfW9HL38DdXT1pK7NBCSGEEEKIZuq8CSxWJRSwJckx2friQANTu3gw9Owwp1buWpaM9mNwsGOyNoC7TsP6KwIctvf9MYlD6UUk5DrOFPXFf7mYLOdyLdYklD8UK63QWum9CCGEEEII0dycN6/Q1yc6H/40spUreo3Cr+P8Scgx09Jdi7aS6VxDPbR46RWyi84FC1YV3tqbTXqhY7J2cr6Vb2LyuKOzB0czzRzLLj9x/A5JzhZCCCGEEBegJtNjseRoHv87nMuhdOe5Cxkm5z0B10Wcm3Up1FNXaVABxUOi2jkZlrSkgiFOz+3M5FSehR+P59ltD3LT2KaQNWjg5g7ulV5fCCGEEEKI5qbJ9FjctSEdAK0CCy7x5fJw+2laj2c59hIMCjYQ7lWzWyibZ1GZLJPKXevT2JdmH/g81N2LYS1d2HS6kDGhroRJcrYQQgghhLgANZkeixIWFR76O4OUMjMxHXWySvaTkd41vs6V5ayKXVonH/sgYdNpE5mmc0OlfAwKk9q7c5GvnnskOVsIIYQQQlzAmlxgAcUJ0O/vy7F9zii0klomKXrzVYHlJmhXxQ3t3OlqLD8Q0Gtg+WX+XBygL3efe7t6Yqxmz4cQQgghhBDNUZN9Kv7heD6Ws6teH8+2763o6KOja4vyH/irwlWn8PdVgeWWjw11xd9Vy8PdvcrdZ1QrxzUxhBBCCCGEuBA1mcBCWybnOiHXQsfvThObbeafZPtpZiPqaMiRoih0beH8XHd1KZ7dqVepBfZK02uKV/YWQgghhBBCNKHA4pHuXrTx0tptSy208tT2TJ7bmWW3vX+g84f9mmjlrnXYdl2EG8NCinsjQtw1BLg6NlMPXz2uuspnoBJCCCGEEOJC0GQCi76BeiL9HAOG3+MLyDOfS5hWgGsjKk+8rqoix2UrmNnr3PAnRVHo5efYM9EnoO6CGyGEEEIIIc53TSaw6OVnoF8VeiKGtHSp0yldI50EDe197LdFOgkirqjCrFJCCCGEEEJcKJpMYBHsruXS1q4YKqnRYz3KT6auibLTzn4+rIXDPlM7e+BjKB725KZV+GhIi1rNSCWEEEIIIURz06QWXmjjpeObEX68vieLf844rsC94jJ/BgTV7QN9L38Dnw5twS+x+VzSyoVrIhxXzg5007L3umC2JBUSFegiU8wKIYQQQghRRpMKLADGhLkyJsyVFfH53Lw2jbMzzvJ8X+86DypKXNfOnevaOQYUpfkYNIwLk+FPQgghhBBCONPkAosSl7Z2Y9VlAXwTk0tPPwO3d6r4wV8IIYQQQgjReKo1pmf9+vUYjUY2btwIwP79+xk2bBjBwcGMGzeO2NhY274VlVXVxYEG3h3Ugjs6e6AoMrWrEEIIIYQQTVWVA4uioiIef/xx22er1crkyZOJjIxk165dBAUFcd9991VaJoQQQgghhGh+qjwUat68eYSEhHDy5EkANm/ezPHjx1m9ejV+fn7MmDGDIUOGEBcXx4kTJ8otCw8Pr7ebEUIIIYQQQjSOKvVYJCQk8O677/L666/btm3dupW2bdvi5+cHQLdu3XBxcWHbtm0VlgkhhBBCCCGanyoFFk888QR33nknHTp0sG1LSkqyBQ5QvEK10WgkKSmpwjIhhBBCCCFE81PpUKjVq1ezZ88ePvnkE4cyVVXtPlut1iqVORMTE1NZVUQtSPs2HGnrhiNt3bCkvRuOtHXDkHZuWNLeDae+2rp0J4MzlQYWP/30E0lJSXTu3BmA3NxcbrzxRh599FFSU1Nt+1mtVjIzMwkODsZkMpVbVtOKipqLiYmR9m0g0tYNR9q6YUl7Nxxp64Yh7dywpL0bTmO2tZKRkaFWtENqaio5OTm2zwMGDOD9998nJCSEyy67jJiYGPz9/dmzZw/Dhg1j3759JCQklFsWFhZW7zclhBBCCCGEaFiV5lj4+fkRHh5u+6MoCoGBgURFRREREcELL7xAYmIib775JoMHDyYsLKzCMiGEEEIIIUTzU60F8kpTFIWvvvqK6OhoIiMjSU5OZt68eZWWCSGEEEIIIZqfSodCCSGEEEIIIURlatxjIYQQQgghhBAlGiywSE1NxWg0EhcX11CXvGDs3bsXo9HI/v37G7sqF4zq/j53796djRs31nOtmo8dO3YwZMgQgoKCGDVqFP/++29jV+mCId/V9Uu+rxuefF/XP/nObjxN7TtbeiyagXXr1tn9V4jzWVZWFjfccAP9+/dn69athIWFMWnSJMxmc6XHjh8/noULFzZALYWoGfm+Fs2NfGeL0iSwaAbWrl3L4MGDWbt2bWNXRYhaW7lyJXl5ebz00ku0bduWF198kWPHjrF9+/bGrpoQtSbf16K5ke9sUVqjBBbbtm1jyJAhtGzZkhEjRti6zOLi4jAajfzzzz/069ePsLAw5syZ0xhVPG/k5+ezbds2HnzwQbZs2UJBQQEbN26kW7du3H///YSHhzN69GgOHz5sO2bu3LlMmzaNdevWMXToUJ566qlGvIPzn9FotPss3ei1c+DAASIiInBxcQGgVatWeHl5cejQIfbv38/YsWNp1aoVo0aNYs+ePQA88MADGI1G/v77b6ZPn47RaGTatGmNeRvNgnxX1y35vm588n1d9+Q7u+loCt/ZDR5YqKrKbbfdxoQJE4iOjmbgwIHMnj3bbp+ZM2cyf/585s+fzzvvvENsbGxDV/O88ffff+Pu7s7o0aPx8vJi8+bNACQkJODm5saGDRvo2rUrd955J6p6bgKwQ4cOMWvWLB577DGmTp3aWNUXwkF6ejre3t5227y9vTlz5gzXXHMNI0eO5J9//mHw4MHceuutALz22mvExsYSFRXFG2+8QWxsLG+++WZjVL/ZkO/quiff16I5ku/spqGpfGfr6vyMVbBmzRoCAgI4dOgQeXl5xMTE2JU//vjj9OnTB4CgoCASEhJo06ZNI9S06Vu7di0XX3wxiqJw8cUXs3btWsaOHYvBYGDOnDm4u7sze/Zs2rdvT3x8POHh4QAcPHiQ7du3S7uK84Kqquh0Ojw8PPi///s/AB577DG6d+9OUVERbm5uuLm5odVqcXNzc3grKWpGvqvrlnxfiwuFfGc3jqbwnV1vPRa7d+9mxIgRDtt1Oh2ff/45nTt3Zvr06Zw+fRqLxWK3z6BBg2x/1+v1dm9uhL1169axbt06WrduzZo1a2zjdlu0aIG7uzsA/v7+6PV6zpw5Yztu3Lhx8j+paijv9/nkyZMO2/Ly8hqiSs2Wr68vmZmZdtuys7OxWCy0bt3ats3Ly4trrrkGvV7f0FVsVuS7uuHI93XDkO/rhiXf2Q2rqX9n11tg4e7uTnR0NEVFRQCkpaWhKAqxsbF89NFH/P3332zatInbbrvN4diyXWrCuVOnTnHo0CG+/fZbNm7cyLfffsvBgwdJSkoiNTWVnJwcAJKTkykqKiIoKMh2rIeHR2NV+7xU3u9zaGgogO0fb3x8PKmpqY1Wz+agW7duHDt2jIKCAqC4TbOzs9FqtcTHx9v2M5vNDBo0iKNHj9q2aTQaebitJvmubhjyfd1w5Pu6Ycl3dsNq6t/Z9RZYtG3blpYtW/LOO+9w8uRJ5s2bx6BBg8jOzkZRFHJycti2bRtPP/20/FLV0Nq1a/H19WXkyJGEh4czYsQIfH19KSoqwmw289xzzxEfH88LL7xAZGQkYWFhjV3l81Z5v88BAQHo9Xq2bduG2Wxm1qxZGAyGxq7ueW3s2LF4eHgwa9YsYmNjeeaZZ4iIiOCuu+4iJyeH1157jcTERN58802ysrLsfq/bt2/Phg0bSEpKYtOmTRQWFjbinZwf5Lu6Ycj3dcOR7+uGJd/ZDaupf2fXW2BhMBj48sv/b+/+Q6uq/ziOP28r3azhlUVuS2mzH8RKx4VcY+osXSTGigosjC1cLXKrTDKjqCgMxqBZhkgrTCpCLSJqC7EpBiPbLDR3V+jS0iRxw3DDH6uxtu8f8t2XoZvr3rPNb3s+YP+ce98f3meM1877Hu75bKCmpoacnBwOHjzIW2+9RUFBAXfeeSe33XYby5cvp6ioiGPHjtHW1jZcrfxrff311+Tn5xMKhYCzk39+fj6rVq1iypQp9Pb2kpuby759+3j33XdHudv/bwP9PSclJfHKK6+wZMkS8vPzueOOO/p90qh/Ljk5mc2bN9PY2EhOTg5Hjhzho48+YuLEiXzyySds27aNmTNnsmPHDjZu3NjvwmDFihUcOXKEGTNmUFZWNqTnqI91ZvXIMK9Hjnk9sszskXWxZ3aovb3dj6D+Zerr6ykrKyMajY52K5KkQZjXkv5N3CBPkiRJUty8YyFJkiQpbt6xkCRJkhS3QAeLw4cPU1hYyJQpU5g3bx4//vgjAM3NzcydO5fU1FQWLFhwzk5/x44d46677mLPnj39jn///ffMnj2btLQ0Fi1axIkTJ4JsV5LGrFjyeqCaC9VJkmIXdF7DwNfe8Qp0sHj66adJS0tj586dXH311ZSWltLT00NxcTGRSITdu3czefJkysrK+mqeeOIJbrzxRr755pt+a3V3d7NkyRJycnJoaGjgzz//5Nlnnw2yXUkas2LJ6/PVABeskyTFLsi8hoGvvYMQ2Hcs/ruhT319PTfddBNfffUVixYtora2lsLCQg4cOEBKSgrRaJQ5c+awd+9errnmmr6NgbKzs9mxYweRSASAffv2kZubS319PdOnT2fbtm089NBDHD58mMTExCBalqQxKZa8Tk9PP2/NoUOHaG5uHjTnJUmxCTqvw+HwgNfeQQjsjkVXVxevvvoqGRkZwP92AmxoaCAzM5OUlBTg7A6N48ePp7GxEYCUlJTz/uPp7OwEYPz48cDZLeP/+usvfv3116BalqQxKZa8Hqhm3LhxF8x5SVJsgs5rGPjaOwiXBrXQ5ZdfzpNPPgmcna6qq6u59957aW1t7TtpgFAoRDgcprW1ddD1pk2bxoQJE9i6dSvXXXcdmzZtAqCjoyOoliVpTIolrweqmTBhQsw5L0kaXNB5PdwCfypUd3c3paWlnD59mqqqKoBzthTv6em54DoTJ06koqKib1fSyy67DGBEfimSNBbEktfnqxlKnSQpdkHm9XAKdLDo6emhpKSE/fv3U1NTw6RJk0hNTeWPP/7o956Ojg5SU1MvuN7DDz/Mb7/9xv79+1m4cCEAkydPDrJlSRqTYsnr89UAceW8JGlwQeb1cAt0sKisrKSlpYUvv/yybwDIy8vj0KFDHD9+HIBoNEpXVxe5ubmDrnXq1Cnuv/9+Tp48SXJyMrt37+baa691sJCkAMSS1+erGUqdJCl2Qeb1cAtssGhtbWXdunWsXr2aSy65hPb2dtrb27nllluYNm0aq1at4ujRo1RVVTF79mymTp066HpXXHEFv//+O++88w4///wz69evp7i4OKh2JWnMiiWvB6rp7u4mNzc3ppyXJA0u6LweboENFtu3b+fkyZMsXLiQjIyMvp+Ghgbef/99fvjhByKRCG1tbaxdu3ZIa1ZXV1NbW8vtt9/OvHnzfC66JAUglrweqObbb78lFArFnPOSpIEFndfDLbB9LCRJkiSNXYE/FUqSJEnS2ONgIUmSJCluDhaSJEmS4uZgIUmSJCluDhaSJEmS4uZgIUk6R319PeFwmHA4THp6OnPmzGHdunX09PSMdmuSpIuUg4UkaUBvv/02H3zwAdnZ2bzwwgssW7bsH9U3NTVRUVExTN1Jki4mDhaSpAFlZWVRUFDA2rVrKS0t5cMPP6SpqWnI9dFolMrKymHsUJJ0sXCwkCQNyaOPPgrAli1bRrkTSdLFyMFCkjQkN9xwA4mJibS0tADw2WefkZeXR1paGtnZ2VRXV/e9d+nSpYTDYcrLywH6vq+xdOnSfmvW1tYya9YsUlNTmTVrFnV1dSN3QpKkQDlYSJKGJBQKEQ6HaW9vZ9euXTzyyCPk5+fz8ccfU1RUxHPPPcfOnTsBWLlyJXV1daxYsQKAuro66urqWLlyZd96W7dupaioiIKCAjZv3kxeXh4PPvgge/fuHZXzkyTF59LRbkCS9P8jFArR29tLYmIia9asYfHixSQkJHDzzTezevVqvvvuO/Ly8sjMzCQzM7Pv7sbMmTPPWauqqor58+fz0ksvAZCXl8eWLVvYtGkT2dnZI3pekqT4OVhIkoaso6ODSZMmMWPGDE6fPs2LL77Irl27aG5upqurizNnzgx5rWg0SmdnJ1deeWW/4wcPHgy6bUnSCHCwkCQNyS+//MKZM2fIyspi/fr1PP/88xQXF1NeXs6tt97KggUL/vGaJSUlFBUV9TuWnJwcVMuSpBHkYCFJGpL33nuPUCjE3XffTUlJCffddx+vv/46AG1tbZw4ceKcmsTERAA6OztJSkrq99r06dNpbW0lEon0HXvjjTdISEjgqaeeGsYzkSQNBwcLSdKAfvrpJ44fP05NTQ0bNmzgmWee4frrryclJYXGxka2b99Oa2srVVVVnDp1ir///rtffSQSISEhgTfffJO5c+fS1NTEPffcQ1paGsuXL2fx4sW8/PLLzJ8/n6amJl577TXWrFkzSmcrSYpHqL29vXe0m5AkXVzq6+spLCwEICkpiaysLB577DEeeOABAFpaWli2bBl79uzhqquu4vHHH+fTTz9l3Lhx5+xzsXHjRioqKjh69CgZGRl88cUXpKenA/D5559TWVnJgQMHmDp1KuXl5ZSUlIzsyUqSAuFgIUmSJClu7mMhSZIkKW4OFpIkSZLi5mAhSZIkKW4OFpIkSZLi5mAhSZIkKW4OFpIkSZLi5mAhSZIkKW4OFpIkSZLi9h8+K7jdHzcrAgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 以不同颜色为训练集和测试集绘图\n",
    "df_app[\"Activation\"][:'2020-09-30'].plot(figsize=(12,4),legend=True) #训练集\n",
    "df_app[\"Activation\"]['2020-10-01':].plot(figsize=(12,4),legend=True) #测试集\n",
    "plt.legend(['Training set (Before October 2020)','Test set (2020 October and beyond)']) #图例\n",
    "plt.title('App Activation Count') #图题\n",
    "plt.show() #绘图"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 特征工程"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler #导入归一化缩放器\n",
    "Scaler = MinMaxScaler(feature_range=(0,1)) #创建缩放器\n",
    "Train = Scaler.fit_transform(Train) #拟合缩放器并对训练集进行归一化"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 构建特征集和标签集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 创建具有 60 个时间步长和 1 个输出的数据结构 - 训练集\n",
    "X_train = [] #初始化\n",
    "y_train = [] #初始化\n",
    "for i in range(60,Train.size): \n",
    "    X_train.append(Train[i-60:i,0]) #构建特征\n",
    "    y_train.append(Train[i,0]) #构建标签\n",
    "X_train, y_train = np.array(X_train), np.array(y_train) #转换为NumPy数组\n",
    "X_train = np.reshape(X_train, (X_train.shape[0],X_train.shape[1],1)) #转换成神经网络所需的张量形状"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "TrainTest = df_app[\"Activation\"][:] #整体数据\n",
    "inputs = TrainTest[len(TrainTest)-len(Test) - 60:].values #Test加上前60个时间步\n",
    "inputs = inputs.reshape(-1,1) #转换形状\n",
    "inputs  = Scaler.transform(inputs) #归一化\n",
    "# 创建具有 60 个时间步长和 1 个输出的数据结构 - 测试集\n",
    "X_test = [] #初始化\n",
    "y_test = [] #初始化\n",
    "for i in range(60,inputs.size): \n",
    "    X_test.append(inputs[i-60:i,0]) #构建特征\n",
    "    y_test.append(inputs[i,0]) #构建标签\n",
    "X_test = np.array(X_test) #转换为NumPy数组\n",
    "X_test = np.reshape(X_test, (X_test.shape[0],X_test.shape[1],1)) #转换成神经网络所需的张量形状"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 选择算法\n",
    "\n",
    "这里我们采用RNN神经网络两种算法"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 神经网络模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#!pip install keras\n",
    "#!pip install tensorflow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "lstm (LSTM)                  (None, 60, 50)            10400     \n",
      "_________________________________________________________________\n",
      "lstm_1 (LSTM)                (None, 60, 50)            20200     \n",
      "_________________________________________________________________\n",
      "lstm_2 (LSTM)                (None, 60, 50)            20200     \n",
      "_________________________________________________________________\n",
      "lstm_3 (LSTM)                (None, 50)                20200     \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 1)                 51        \n",
      "=================================================================\n",
      "Total params: 71,051\n",
      "Trainable params: 71,051\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "from keras.models import Sequential #导入序贯模型\n",
    "from keras.layers import Dense, LSTM #导入全连接层和LSTM层\n",
    "# from keras.optimizers import SGD\n",
    "# LSTM网络架构\n",
    "RNN_LSTM = Sequential() #序贯模型\n",
    "RNN_LSTM.add(LSTM(units=50, return_sequences=True, input_shape=(X_train.shape[1],1))) #输入层LSTM\n",
    "RNN_LSTM.add(LSTM(units=50, return_sequences=True)) #中间1层LSTM\n",
    "RNN_LSTM.add(LSTM(units=50, return_sequences=True)) #中间2层LSTM\n",
    "RNN_LSTM.add(LSTM(units=50)) #中间3层LSTM\n",
    "RNN_LSTM.add(Dense(units=1)) #输出层Dense\n",
    "RNN_LSTM.compile(optimizer='rmsprop',loss='mean_squared_error') #编译网络\n",
    "RNN_LSTM.summary() #输出神经网络结构信息"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "8/8 [==============================] - 15s 526ms/step - loss: 0.0654 - val_loss: 0.0082\n",
      "Epoch 2/50\n",
      "8/8 [==============================] - 2s 230ms/step - loss: 0.0033 - val_loss: 0.0031\n",
      "Epoch 3/50\n",
      "8/8 [==============================] - 2s 242ms/step - loss: 0.0086 - val_loss: 0.0092\n",
      "Epoch 4/50\n",
      "8/8 [==============================] - 2s 245ms/step - loss: 0.0070 - val_loss: 0.0074\n",
      "Epoch 5/50\n",
      "8/8 [==============================] - 2s 244ms/step - loss: 0.0058 - val_loss: 0.0100\n",
      "Epoch 6/50\n",
      "8/8 [==============================] - 2s 218ms/step - loss: 0.0091 - val_loss: 0.0053\n",
      "Epoch 7/50\n",
      "8/8 [==============================] - 2s 215ms/step - loss: 0.0080 - val_loss: 0.0044\n",
      "Epoch 8/50\n",
      "8/8 [==============================] - 2s 189ms/step - loss: 0.0065 - val_loss: 0.0051\n",
      "Epoch 9/50\n",
      "8/8 [==============================] - 2s 205ms/step - loss: 0.0078 - val_loss: 0.0023\n",
      "Epoch 10/50\n",
      "8/8 [==============================] - 2s 216ms/step - loss: 0.0064 - val_loss: 0.0025\n",
      "Epoch 11/50\n",
      "8/8 [==============================] - 2s 209ms/step - loss: 0.0044 - val_loss: 0.0030\n",
      "Epoch 12/50\n",
      "8/8 [==============================] - 2s 213ms/step - loss: 0.0069 - val_loss: 0.0156\n",
      "Epoch 13/50\n",
      "8/8 [==============================] - 2s 192ms/step - loss: 0.0029 - val_loss: 0.0058\n",
      "Epoch 14/50\n",
      "8/8 [==============================] - 2s 214ms/step - loss: 0.0067 - val_loss: 0.0038\n",
      "Epoch 15/50\n",
      "8/8 [==============================] - 2s 257ms/step - loss: 0.0052 - val_loss: 0.0059\n",
      "Epoch 16/50\n",
      "8/8 [==============================] - 2s 245ms/step - loss: 0.0043 - val_loss: 0.0043\n",
      "Epoch 17/50\n",
      "8/8 [==============================] - 2s 224ms/step - loss: 0.0024 - val_loss: 0.0029\n",
      "Epoch 18/50\n",
      "8/8 [==============================] - 2s 246ms/step - loss: 0.0071 - val_loss: 0.0042\n",
      "Epoch 19/50\n",
      "8/8 [==============================] - 2s 224ms/step - loss: 0.0039 - val_loss: 0.0023\n",
      "Epoch 20/50\n",
      "8/8 [==============================] - 2s 208ms/step - loss: 0.0035 - val_loss: 0.0026\n",
      "Epoch 21/50\n",
      "8/8 [==============================] - 2s 226ms/step - loss: 0.0058 - val_loss: 0.0065\n",
      "Epoch 22/50\n",
      "8/8 [==============================] - 2s 223ms/step - loss: 0.0031 - val_loss: 0.0020\n",
      "Epoch 23/50\n",
      "8/8 [==============================] - 2s 262ms/step - loss: 0.0038 - val_loss: 0.0023\n",
      "Epoch 24/50\n",
      "8/8 [==============================] - 2s 250ms/step - loss: 0.0042 - val_loss: 0.0061\n",
      "Epoch 25/50\n",
      "8/8 [==============================] - 2s 233ms/step - loss: 0.0026 - val_loss: 0.0642\n",
      "Epoch 26/50\n",
      "8/8 [==============================] - 2s 221ms/step - loss: 0.0061 - val_loss: 0.0115\n",
      "Epoch 27/50\n",
      "8/8 [==============================] - 2s 220ms/step - loss: 0.0030 - val_loss: 0.0318\n",
      "Epoch 28/50\n",
      "8/8 [==============================] - 2s 258ms/step - loss: 0.0045 - val_loss: 0.0252\n",
      "Epoch 29/50\n",
      "8/8 [==============================] - 2s 214ms/step - loss: 0.0028 - val_loss: 0.0097\n",
      "Epoch 30/50\n",
      "8/8 [==============================] - 1s 184ms/step - loss: 0.0061 - val_loss: 0.0127\n",
      "Epoch 31/50\n",
      "8/8 [==============================] - 2s 212ms/step - loss: 0.0014 - val_loss: 0.0163\n",
      "Epoch 32/50\n",
      "8/8 [==============================] - 2s 208ms/step - loss: 0.0054 - val_loss: 0.0164\n",
      "Epoch 33/50\n",
      "8/8 [==============================] - 2s 200ms/step - loss: 0.0028 - val_loss: 0.0130\n",
      "Epoch 34/50\n",
      "8/8 [==============================] - 2s 210ms/step - loss: 0.0045 - val_loss: 0.0184\n",
      "Epoch 35/50\n",
      "8/8 [==============================] - 2s 211ms/step - loss: 0.0027 - val_loss: 0.0019\n",
      "Epoch 36/50\n",
      "8/8 [==============================] - 2s 212ms/step - loss: 0.0040 - val_loss: 0.0023\n",
      "Epoch 37/50\n",
      "8/8 [==============================] - 2s 244ms/step - loss: 0.0017 - val_loss: 0.0045\n",
      "Epoch 38/50\n",
      "8/8 [==============================] - 2s 272ms/step - loss: 0.0039 - val_loss: 0.0037\n",
      "Epoch 39/50\n",
      "8/8 [==============================] - 2s 208ms/step - loss: 0.0028 - val_loss: 0.0292\n",
      "Epoch 40/50\n",
      "8/8 [==============================] - 2s 253ms/step - loss: 0.0030 - val_loss: 0.0296\n",
      "Epoch 41/50\n",
      "8/8 [==============================] - 2s 245ms/step - loss: 0.0039 - val_loss: 0.0086\n",
      "Epoch 42/50\n",
      "8/8 [==============================] - 2s 273ms/step - loss: 0.0022 - val_loss: 0.0185\n",
      "Epoch 43/50\n",
      "8/8 [==============================] - 2s 295ms/step - loss: 0.0035 - val_loss: 0.0114\n",
      "Epoch 44/50\n",
      "8/8 [==============================] - 2s 300ms/step - loss: 0.0022 - val_loss: 0.0299\n",
      "Epoch 45/50\n",
      "8/8 [==============================] - 2s 271ms/step - loss: 0.0036 - val_loss: 0.0125\n",
      "Epoch 46/50\n",
      "8/8 [==============================] - 2s 252ms/step - loss: 0.0025 - val_loss: 0.0035\n",
      "Epoch 47/50\n",
      "8/8 [==============================] - 2s 218ms/step - loss: 0.0028 - val_loss: 0.0062\n",
      "Epoch 48/50\n",
      "8/8 [==============================] - 2s 235ms/step - loss: 0.0026 - val_loss: 0.0121\n",
      "Epoch 49/50\n",
      "8/8 [==============================] - 2s 228ms/step - loss: 0.0028 - val_loss: 0.0114\n",
      "Epoch 50/50\n",
      "8/8 [==============================] - 2s 246ms/step - loss: 0.0022 - val_loss: 0.0117\n"
     ]
    }
   ],
   "source": [
    "# 训练并保存训练历史信息\n",
    "history = RNN_LSTM.fit(X_train, y_train, # 指定训练集\n",
    "                  epochs=50,        # 指定训练的轮次\n",
    "                  batch_size=64,    # 指定数据批量\n",
    "                  validation_split=0.2) #这里直接从训练集数据中拆分验证集，更方便"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def show_history(history): # 显示训练过程中的学习曲线\n",
    "    loss = history.history['loss'] #训练损失\n",
    "    val_loss = history.history['val_loss'] #验证损失\n",
    "    epochs = range(1, len(loss) + 1) #训练轮次\n",
    "    plt.figure(figsize=(12,4)) # 图片大小\n",
    "    plt.subplot(1, 2, 1) #子图1\n",
    "    plt.plot(epochs, loss, 'bo', label='Training loss') #训练损失\n",
    "    plt.plot(epochs, val_loss, 'b', label='Validation loss') #验证损失\n",
    "    plt.title('Training and validation loss') #图题\n",
    "    plt.xlabel('Epochs') #X轴文字\n",
    "    plt.ylabel('Loss') #Y轴文字\n",
    "    plt.legend() #图例\n",
    "    plt.show() #绘图 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_history(history) # 调用绘图函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 定义绘图函数\n",
    "def plot_predictions(test,predicted):\n",
    "    plt.plot(test, color='red',label='Real Count') #真值\n",
    "    plt.plot(predicted, color='blue',label='Predicted Count') #预测值\n",
    "    plt.title('Flower App Activation Prediction') #图题\n",
    "    plt.xlabel('Time') #X轴时间\n",
    "    plt.ylabel('Flower App Activation Count') #Y轴激活数\n",
    "    plt.legend() #图例\n",
    "    plt.show() #绘图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_pred = RNN_LSTM.predict(X_test) #预测\n",
    "Pred = Scaler.inverse_transform(y_pred) #反归一化\n",
    "plot_predictions(Test,Pred) #绘图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math #导入数学函数\n",
    "from sklearn.metrics import mean_squared_error\n",
    "def return_rmse(test,predicted): #定义均方损失函数\n",
    "    rmse = math.sqrt(mean_squared_error(test, predicted)) #均方损失\n",
    "    print(\"MSE损失值 {}.\".format(rmse))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE损失值 0.13970603156988873.\n"
     ]
    }
   ],
   "source": [
    "return_rmse(y_test,y_pred) #计算均方损失"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
